Introduction

What is the structure of your dataset?

The data set has 485,490 rows, and 600+ columns

What is/are the main feature(s) of interest in your dataset?

We have selected PV1MATH, PV1READ, and PV1SCIE; these are the desired features to be invstigated and to observe relationships vs other selected features from the data set

What features in the dataset do you think will help support your investigation into your feature(s) of interest?

We will explore the following features :

  • 1st section
  • time spent learning math, reading and science, time spent using computer, Gender, Class Size, score by country

  • 2nd section part a)
  • Possessions - cellular phones, televisions, computers, How many books at home

  • 2nd section part b)
  • Subjective Norms -Friends Do Well in Mathematics, Friends Work Hard on Mathematics, Friends Enjoy Mathematics Tests, Parents >Believe Studying Mathematics Is Important, Parents Believe Mathematics Is Important for Career, Parents Like Mathematics

    Importing Data

    In [1]:
    # import all packages and set plots to be embedded inline
    import numpy as np
    import pandas as pd
    import matplotlib.pyplot as plt
    import seaborn as sns
    from pandas_profiling import ProfileReport
    import plotly.graph_objects as go
    import pycountry
    
    
    %matplotlib inline
    sns.set_style('whitegrid')
    
    In [11]:
    #import data
    path= "D:\\udacity\\Data Analyst Nano Degree\\Lesson 3\\Project\\communicate-data-project-template\\pisa2012.csv"
    raw_data = pd.read_csv(path, encoding = "ISO-8859-1");
    
    C:\Users\mostafa.elmallah\Anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3071: DtypeWarning: Columns (15,16,17,21,22,23,24,25,26,30,31,36,37,45,65,123,155,156,157,158,159,160,161,162,163,164,165,166,167,168,169,170,171,284,285,286,287,288,289,290,291,292,293,294,295,296,297,298,299,300,301,302,303,307,308,309,310,311,312,313,314,315,316,317,318,319,320,321,322,323,324,325,326,327,328,329,330,331,332,333,334,335,336,337,338,339,340,341,342,343,344,345,346,347,348,349,350,351,352,353,354,355,356,357,376,377,378,379,380,381,382,383,384,385,386,387,388,389,390,391,392,393,394,395,396,397,398,399,400,401,402,403,475) have mixed types.Specify dtype option on import or set low_memory=False.
      has_raised = await self.run_ast_nodes(code_ast.body, cell_name,
    
    In [12]:
    # drop unnecessary column
    raw_data.drop('Unnamed: 0', axis=1,inplace=True)
    
    In [13]:
    # load and change the column names from data dictionary file
    
    path2= "D:\\udacity\\Data Analyst Nano Degree\\Lesson 3\\Project\\communicate-data-project-template\\pisadict2012.csv"
    coloumn_dict= pd.read_csv(path2, encoding = "ISO-8859-1")
    
    In [27]:
    raw_data.columns= coloumn_dict['x']
    
    In [32]:
    # view al columns
    pd.options.display.max_columns = None
    raw_data.head()
    
    Out[32]:
    x Country code 3-character Adjudicated sub-region code 7-digit code (3-digit country code + region ID + stratum ID) Stratum ID 7-character (cnt + region ID + original stratum ID) OECD country National Centre 6-digit Code School ID 7-digit (region ID + stratum ID + 3-digit school ID) Student ID International Grade National Study Programme Birth - Month Birth -Year Gender Attend <ISCED 0> Age at <ISCED 1> Repeat - <ISCED 1> Repeat - <ISCED 2> Repeat - <ISCED 3> Truancy - Late for School Truancy - Skip whole school day Truancy - Skip classes within school day At Home - Mother At Home - Father At Home - Brothers At Home - Sisters At Home - Grandparents At Home - Others Mother<Highest Schooling> Mother Qualifications - <ISCED level 6> Mother Qualifications - <ISCED level 5A> Mother Qualifications - <ISCED level 5B> Mother Qualifications - <ISCED level 4> Mother Current Job Status Father<Highest Schooling> Father Qualifications - <ISCED level 6> Father Qualifications - <ISCED level 5A> Father Qualifications - <ISCED level 5B> Father Qualifications - <ISCED level 4> Father Current Job Status Country of Birth International - Self Country of Birth International - Mother Country of Birth International - Father Age of arrival in <country of test> International Language at Home Possessions - desk Possessions - own room Possessions - study place Possessions - computer Possessions - software Possessions - Internet Possessions - literature Possessions - poetry Possessions - art Possessions - textbooks Possessions - <technical reference books> Possessions - dictionary Possessions - dishwasher Possessions - <DVD> Possessions - <Country item 1> Possessions - <Country item 2> Possessions - <Country item 3> How many - cellular phones How many - televisions How many - computers How many - cars How many - rooms bath or shower How many books at home Math Interest - Enjoy Reading Instrumental Motivation - Worthwhile for Work Math Interest - Look Forward to Lessons Math Interest - Enjoy Maths Instrumental Motivation - Worthwhile for Career Chances Math Interest - Interested Instrumental Motivation - Important for Future Study Instrumental Motivation - Helps to Get a Job Subjective Norms -Friends Do Well in Mathematics Subjective Norms -Friends Work Hard on Mathematics Subjective Norms - Friends Enjoy Mathematics Tests Subjective Norms - Parents Believe Studying Mathematics Is Important Subjective Norms - Parents Believe Mathematics Is Important for Career Subjective Norms - Parents Like Mathematics Math Self-Efficacy - Using a <Train Timetable> Math Self-Efficacy - Calculating TV Discount Math Self-Efficacy - Calculating Square Metres of Tiles Math Self-Efficacy - Understanding Graphs in Newspapers Math Self-Efficacy - Solving Equation 1 Math Self-Efficacy - Distance to Scale Math Self-Efficacy - Solving Equation 2 Math Self-Efficacy - Calculate Petrol Consumption Rate Math Anxiety - Worry That It Will Be Difficult Math Self-Concept - Not Good at Maths Math Anxiety - Get Very Tense Math Self-Concept- Get Good <Grades> Math Anxiety - Get Very Nervous Math Self-Concept - Learn Quickly Math Self-Concept - One of Best Subjects Math Anxiety - Feel Helpless Math Self-Concept - Understand Difficult Work Math Anxiety - Worry About Getting Poor <Grades> Perceived Control - Can Succeed with Enough Effort Perceived Control - Doing Well is Completely Up to Me Perceived Control - Family Demands and Problems Perceived Control - Different Teachers Perceived Control - If I Wanted I Could Perform Well Perceived Control - Perform Poorly Regardless Attributions to Failure - Not Good at Maths Problems Attributions to Failure - Teacher Did Not Explain Well Attributions to Failure - Bad Guesses Attributions to Failure - Material Too Hard Attributions to Failure - Teacher Didnt Get Students Interested Attributions to Failure - Unlucky Math Work Ethic - Homework Completed in Time Math Work Ethic - Work Hard on Homework Math Work Ethic - Prepared for Exams Math Work Ethic - Study Hard for Quizzes Math Work Ethic - Study Until I Understand Everything Math Work Ethic - Pay Attention in Classes Math Work Ethic - Listen in Classes Math Work Ethic - Avoid Distractions When Studying Math Work Ethic - Keep Work Organized Math Intentions - Mathematics vs. Language Courses After School Math Intentions - Mathematics vs. Science Related Major in College Math Intentions - Study Harder in Mathematics vs. Language Classes Math Intentions - Take Maximum Number of Mathematics vs. Science Classes Math Intentions - Pursuing a Career That Involves Mathematics vs. Science Math Behaviour - Talk about Maths with Friends Math Behaviour - Help Friends with Maths Math Behaviour - <Extracurricular> Activity Math Behaviour - Participate in Competitions Math Behaviour - Study More Than 2 Extra Hours a Day Math Behaviour - Play Chess Math Behaviour - Computer programming Math Behaviour - Participate in Math Club Learning Strategies- Important Parts vs. Existing Knowledge vs. Learn by Heart Learning Strategies- Improve Understanding vs. New Ways vs. Memory Learning Strategies - Other Subjects vs. Learning Goals vs. Rehearse Problems Learning Strategies - Repeat Examples vs. Everyday Applications vs. More Information Out of school lessons - <test lang> Out of school lessons - <maths> Out of school lessons - <science> Out of school lessons - other Out-of-School Study Time - Homework Out-of-School Study Time - Guided Homework Out-of-School Study Time - Personal Tutor Out-of-School Study Time - Commercial Company Out-of-School Study Time - With Parent Out-of-School Study Time - Computer Experience with Applied Maths Tasks - Use <Train Timetable> Experience with Applied Maths Tasks - Calculate Price including Tax Experience with Applied Maths Tasks - Calculate Square Metres Experience with Applied Maths Tasks - Understand Scientific Tables Experience with Pure Maths Tasks - Solve Equation 1 Experience with Applied Maths Tasks - Use a Map to Calculate Distance Experience with Pure Maths Tasks - Solve Equation 2 Experience with Applied Maths Tasks - Calculate Power Consumption Rate Experience with Applied Maths Tasks - Solve Equation 3 Familiarity with Math Concepts - Exponential Function Familiarity with Math Concepts - Divisor Familiarity with Math Concepts - Quadratic Function Overclaiming - Proper Number Familiarity with Math Concepts - Linear Equation Familiarity with Math Concepts - Vectors Familiarity with Math Concepts - Complex Number Familiarity with Math Concepts - Rational Number Familiarity with Math Concepts - Radicals Overclaiming - Subjunctive Scaling Familiarity with Math Concepts - Polygon Overclaiming - Declarative Fraction Familiarity with Math Concepts - Congruent Figure Familiarity with Math Concepts - Cosine Familiarity with Math Concepts - Arithmetic Mean Familiarity with Math Concepts - Probability Min in <class period> - <test lang> Min in <class period> - <Maths> Min in <class period> - <Science> No of <class period> p/wk - <test lang> No of <class period> p/wk - <Maths> No of <class period> p/wk - <Science> No of ALL <class period> a week Class Size - No of Students in <Test Language> Class OTL - Algebraic Word Problem in Math Lesson OTL - Algebraic Word Problem in Tests OTL - Procedural Task in Math Lesson OTL - Procedural Task in Tests OTL - Pure Math Reasoning in Math Lesson OTL - Pure Math Reasoning in Tests OTL - Applied Math Reasoning in Math Lesson OTL - Applied Math Reasoning in Tests Math Teaching - Teacher shows interest Math Teaching - Extra help Math Teaching - Teacher helps Math Teaching - Teacher continues Math Teaching - Express opinions Teacher-Directed Instruction - Sets Clear Goals Teacher-Directed Instruction - Encourages Thinking and Reasoning Student Orientation - Differentiates Between Students When Giving Tasks Student Orientation - Assigns Complex Projects Formative Assessment - Gives Feedback Teacher-Directed Instruction - Checks Understanding Student Orientation - Has Students Work in Small Groups Teacher-Directed Instruction - Summarizes Previous Lessons Student Orientation - Plans Classroom Activities Formative Assessment - Gives Feedback on Strengths and Weaknesses Formative Assessment - Informs about Expectations Teacher-Directed Instruction - Informs about Learning Goals Formative Assessment - Tells How to Get Better Cognitive Activation - Teacher Encourages to Reflect Problems Cognitive Activation - Gives Problems that Require to Think Cognitive Activation - Asks to Use Own Procedures Cognitive Activation - Presents Problems with No Obvious Solutions Cognitive Activation - Presents Problems in Different Contexts Cognitive Activation - Helps Learn from Mistakes Cognitive Activation - Asks for Explanations Cognitive Activation - Apply What We Learned Cognitive Activation - Problems with Multiple Solutions Disciplinary Climate - Students DonÂ’t Listen Disciplinary Climate - Noise and Disorder Disciplinary Climate - Teacher Has to Wait Until its Quiet Disciplinary Climate - Students DonÂ’t Work Well Disciplinary Climate - Students Start Working Late Vignette Teacher Support -Homework Every Other Day/Back in Time Vignette Teacher Support - Homework Once a Week/Back in Time Vignette Teacher Support - Homework Once a Week/Not Back in Time Teacher Support - Lets Us Know We Have to Work Hard Teacher Support - Provides Extra Help When Needed Teacher Support - Helps Students with Learning Teacher Support - Gives Opportunity to Express Opinions Vignette Classroom Management - Students Frequently Interrupt/Teacher Arrives Early Vignette Classroom Management - Students Are Calm/Teacher Arrives on Time Vignette Classroom Management - Students Frequently Interrupt/Teacher Arrives Late Classroom Management - Students Listen Classroom Management - Teacher Keeps Class Orderly Classroom Management - Teacher Starts On Time Classroom Management - Wait Long to <Quiet Down> Student-Teacher Relation - Get Along with Teachers Student-Teacher Relation - Teachers Are Interested Student-Teacher Relation - Teachers Listen to Students Student-Teacher Relation - Teachers Help Students Student-Teacher Relation - Teachers Treat Students Fair Sense of Belonging - Feel Like Outsider Sense of Belonging - Make Friends Easily Sense of Belonging - Belong at School Sense of Belonging - Feel Awkward at School Sense of Belonging - Liked by Other Students Sense of Belonging - Feel Lonely at School Sense of Belonging - Feel Happy at School Sense of Belonging - Things Are Ideal at School Sense of Belonging - Satisfied at School Attitude towards School - Does Little to Prepare Me for Life Attitude towards School - Waste of Time Attitude towards School - Gave Me Confidence Attitude towards School- Useful for Job Attitude toward School - Helps to Get a Job Attitude toward School - Prepare for College Attitude toward School - Enjoy Good Grades Attitude toward School - Trying Hard is Important Perceived Control - Can Succeed with Enough Effort Perceived Control - My Choice Whether I Will Be Good Perceived Control - Problems Prevent from Putting Effort into School Perceived Control - Different Teachers Would Make Me Try Harder Perceived Control - Could Perform Well if I Wanted Perceived Control - Perform Poor Regardless Perseverance - Give up easily Perseverance - Put off difficult problems Perseverance - Remain interested Perseverance - Continue to perfection Perseverance - Exceed expectations Openness for Problem Solving - Can Handle a Lot of Information Openness for Problem Solving - Quick to Understand Openness for Problem Solving - Seek Explanations Openness for Problem Solving - Can Link Facts Openness for Problem Solving - Like to Solve Complex Problems Problem Text Message - Press every button Problem Text Message - Trace steps Problem Text Message - Manual Problem Text Message - Ask a friend Problem Route Selection - Read brochure Problem Route Selection - Study map Problem Route Selection - Leave it to brother Problem Route Selection - Just drive Problem Ticket Machine - Similarities Problem Ticket Machine - Try buttons Problem Ticket Machine - Ask for help Problem Ticket Machine - Find ticket office At Home - Desktop Computer At Home - Portable laptop At Home - Tablet computer At Home - Internet connection At Home - Video games console At Home - Cell phone w/o Internet At Home - Cell phone with Internet At Home - Mp3/Mp4 player At Home - Printer At Home - USB (memory) stick At Home - Ebook reader At school - Desktop Computer At school - Portable laptop At school - Tablet computer At school - Internet connection At school - Printer At school - USB (memory) stick At school - Ebook reader First use of computers First access to Internet Internet at School Internet out-of-school - Weekday Internet out-of-school - Weekend Out-of-school 8 - One player games. Out-of-school 8 - ColLabourative games. Out-of-school 8 - Use email Out-of-school 8 - Chat on line Out-of-school 8 - Social networks Out-of-school 8 - Browse the Internet for fun Out-of-school 8 - Read news Out-of-school 8 - Obtain practical information from the Internet Out-of-school 8 - Download music Out-of-school 8 - Upload content Out-of-school 9 - Internet for school Out-of-school 9 - Email students Out-of-school 9 - Email teachers Out-of-school 9 - Download from School Out-of-school 9 - Announcements Out-of-school 9 - Homework Out-of-school 9 - Share school material At School - Chat on line At School - Email At School - Browse for schoolwork At School - Download from website At School - Post on website At School - Simulations At School - Practice and drilling At School - Homework At School - Group work Maths lessons - Draw graph Maths lessons - Calculation with numbers Maths lessons - Geometric figures Maths lessons - Spreadsheet Maths lessons - Algebra Maths lessons - Histograms Maths lessons - Change in graphs Attitudes - Useful for schoolwork Attitudes - Homework more fun Attitudes - Source of information Attitudes - Troublesome Attitudes - Not suitable for schoolwork Attitudes - Too unreliable Miss 2 months of <ISCED 1> Miss 2 months of <ISCED 2> Future Orientation - Internship Future Orientation - Work-site visits Future Orientation - Job fair Future Orientation - Career advisor at school Future Orientation - Career advisor outside school Future Orientation - Questionnaire Future Orientation - Internet search Future Orientation - Tour<ISCED 3-5> institution Future Orientation - web search <ISCED 3-5> prog Future Orientation - <country specific item> Acquired skills - Find job info - Yes, at school Acquired skills - Find job info - Yes, out of school Acquired skills - Find job info - No, never Acquired skills - Search for job - Yes, at school Acquired skills - Search for job - Yes, out of school Acquired skills - Search for job - No, never Acquired skills - Write resume - Yes, at school Acquired skills - Write resume - Yes, out of school Acquired skills - Write resume - No, never Acquired skills - Job interview - Yes, at school Acquired skills - Job interview - Yes, out of school Acquired skills - Job interview - No, never Acquired skills - ISCED 3-5 programs - Yes, at school Acquired skills - ISCED 3-5 programs - Yes, out of school Acquired skills - ISCED 3-5 programs - No, never Acquired skills - Student financing - Yes, at school Acquired skills - Student financing - Yes, out of school Acquired skills - Student financing - No, never First language learned Age started learning <test language> Language spoken - Mother Language spoken - Father Language spoken - Siblings Language spoken - Best friend Language spoken - Schoolmates Activities language - Reading Activities language - Watching TV Activities language - Internet surfing Activities language - Writing emails Types of support <test language> - remedial lessons Amount of support <test language> Attend lessons <heritage language> - focused Attend lessons <heritage language> - school subjects Instruction in <heritage language> Acculturation - Mother Immigrant (Filter) Acculturation - Enjoy <Host Culture> Friends Acculturation - Enjoy <Heritage Culture> Friends Acculturation - Enjoy <Host Culture> Celebrations Acculturation - Enjoy <Heritage Culture> Celebrations Acculturation - Spend Time with <Host Culture> Friends Acculturation - Spend Time with <Heritage Culture> Friends Acculturation - Participate in <Host Culture> Celebrations Acculturation - Participate in <Heritage Culture> Celebrations Acculturation - Perceived Host-Heritage Cultural Differences - Values Acculturation - Perceived Host-Heritage Cultural Differences - Mother Treatment Acculturation - Perceived Host-Heritage Cultural Differences - Teacher Treatment Calculator Use Effort-real 1 Effort-real 2 Difference in Effort Student Questionnaire Form Booklet ID Standard or simplified set of booklets Age of student Grade compared to modal grade in country Unique national study programme code Mathematics Anxiety Attitude towards School: Learning Outcomes Attitude towards School: Learning Activities Sense of Belonging to School Father SQ ISEI Mother SQ ISEI Mathematics Teacher's Classroom Management Country of Birth National Categories- Father Country of Birth National Categories- Mother Country of Birth National Categories- Self Cognitive Activation in Mathematics Lessons Cultural Distance between Host and Heritage Culture Cultural Possessions Disciplinary Climate ICT Entertainment Use Index of economic, social and cultural status Experience with Applied Mathematics Tasks at School Experience with Pure Mathematics Tasks at School Attributions to Failure in Mathematics Familiarity with Mathematical Concepts Familiarity with Mathematical Concepts (Signal Detection Adjusted) Family Structure Educational level of father (ISCED) Home educational resources Acculturation: Heritage Culture Oriented Strategies Highest educational level of parents Highest parental occupational status Home Possessions ICT Use at Home for School-related Tasks Acculturation: Host Culture Oriented Strategies Attitudes Towards Computers: Limitations of the Computer as a Tool for School Learning Attitudes Towards Computers: Computer as a Tool for School Learning ICT Availability at Home ICT resources ICT Availability at School Immigration status Information about Careers Information about the Labour Market provided by the School Information about the Labour Market provided outside of School Instrumental Motivation for Mathematics Mathematics Interest ISCED designation ISCED level ISCED orientation Preference for Heritage Language in Conversations with Family and Friends Language at home (3-digit code) Preference for Heritage Language in Language Reception and Production Learning time (minutes per week) - <test language> Mathematics Behaviour Mathematics Self-Efficacy Mathematics Intentions Mathematics Work Ethic Educational level of mother (ISCED) Learning time (minutes per week)- <Mathematics> Mathematics Teacher's Support ISCO-08 Occupation code - Mother ISCO-08 Occupation code - Father Openness for Problem Solving Out-of-School Study Time Highest parental education in years Perseverance Grade Repetition Mathematics Self-Concept Learning time (minutes per week) - <Science> Teacher Student Relations Subjective Norms in Mathematics Teacher Behaviour: Formative Assessment Teacher Behaviour: Student Orientation Teacher Behaviour: Teacher-directed Instruction Teacher Support Language of the test Time of computer use (mins) Use of ICT in Mathematic Lessons Use of ICT at School Wealth Attitude towards School: Learning Outcomes (Anchored) Attitude towards School: Learning Activities (Anchored) Sense of Belonging to School (Anchored) Mathematics Teacher's Classroom Management (Anchored) Cognitive Activation in Mathematics Lessons (Anchored) Instrumental Motivation for Mathematics (Anchored) Mathematics Interest (Anchored) Mathematics Work Ethic (Anchored) Mathematics Teacher's Support (Anchored) Mathematics Self-Concept (Anchored) Teacher Student Relations (Anchored) Subjective Norms in Mathematics (Anchored) Plausible value 1 in mathematics Plausible value 2 in mathematics Plausible value 3 in mathematics Plausible value 4 in mathematics Plausible value 5 in mathematics Plausible value 1 in content subscale of math - Change and Relationships Plausible value 2 in content subscale of math - Change and Relationships Plausible value 3 in content subscale of math - Change and Relationships Plausible value 4 in content subscale of math - Change and Relationships Plausible value 5 in content subscale of math - Change and Relationships Plausible value 1 in content subscale of math - Quantity Plausible value 2 in content subscale of math - Quantity Plausible value 3 in content subscale of math - Quantity Plausible value 4 in content subscale of math - Quantity Plausible value 5 in content subscale of math - Quantity Plausible value 1 in content subscale of math - Space and Shape Plausible value 2 in content subscale of math - Space and Shape Plausible value 3 in content subscale of math - Space and Shape Plausible value 4 in content subscale of math - Space and Shape Plausible value 5 in content subscale of math - Space and Shape Plausible value 1 in content subscale of math - Uncertainty and Data Plausible value 2 in content subscale of math - Uncertainty and Data Plausible value 3 in content subscale of math - Uncertainty and Data Plausible value 4 in content subscale of math - Uncertainty and Data Plausible value 5 in content subscale of math - Uncertainty and Data Plausible value 1 in process subscale of math - Employ Plausible value 2 in process subscale of math - Employ Plausible value 3 in process subscale of math - Employ Plausible value 4 in process subscale of math - Employ Plausible value 5 in process subscale of math - Employ Plausible value 1 in process subscale of math - Formulate Plausible value 2 in process subscale of math - Formulate Plausible value 3 in process subscale of math - Formulate Plausible value 4 in process subscale of math - Formulate Plausible value 5 in process subscale of math - Formulate Plausible value 1 in process subscale of math - Interpret Plausible value 2 in process subscale of math - Interpret Plausible value 3 in process subscale of math - Interpret Plausible value 4 in process subscale of math - Interpret Plausible value 5 in process subscale of math - Interpret Plausible value 1 in reading Plausible value 2 in reading Plausible value 3 in reading Plausible value 4 in reading Plausible value 5 in reading Plausible value 1 in science Plausible value 2 in science Plausible value 3 in science Plausible value 4 in science Plausible value 5 in science FINAL STUDENT WEIGHT FINAL STUDENT REPLICATE BRR-FAY WEIGHT1 FINAL STUDENT REPLICATE BRR-FAY WEIGHT2 FINAL STUDENT REPLICATE BRR-FAY WEIGHT3 FINAL STUDENT REPLICATE BRR-FAY WEIGHT4 FINAL STUDENT REPLICATE BRR-FAY WEIGHT5 FINAL STUDENT REPLICATE BRR-FAY WEIGHT6 FINAL STUDENT REPLICATE BRR-FAY WEIGHT7 FINAL STUDENT REPLICATE BRR-FAY WEIGHT8 FINAL STUDENT REPLICATE BRR-FAY WEIGHT9 FINAL STUDENT REPLICATE BRR-FAY WEIGHT10 FINAL STUDENT REPLICATE BRR-FAY WEIGHT11 FINAL STUDENT REPLICATE BRR-FAY WEIGHT12 FINAL STUDENT REPLICATE BRR-FAY WEIGHT13 FINAL STUDENT REPLICATE BRR-FAY WEIGHT14 FINAL STUDENT REPLICATE BRR-FAY WEIGHT15 FINAL STUDENT REPLICATE BRR-FAY WEIGHT16 FINAL STUDENT REPLICATE BRR-FAY WEIGHT17 FINAL STUDENT REPLICATE BRR-FAY WEIGHT18 FINAL STUDENT REPLICATE BRR-FAY WEIGHT19 FINAL STUDENT REPLICATE BRR-FAY WEIGHT20 FINAL STUDENT REPLICATE BRR-FAY WEIGHT21 FINAL STUDENT REPLICATE BRR-FAY WEIGHT22 FINAL STUDENT REPLICATE BRR-FAY WEIGHT23 FINAL STUDENT REPLICATE BRR-FAY WEIGHT24 FINAL STUDENT REPLICATE BRR-FAY WEIGHT25 FINAL STUDENT REPLICATE BRR-FAY WEIGHT26 FINAL STUDENT REPLICATE BRR-FAY WEIGHT27 FINAL STUDENT REPLICATE BRR-FAY WEIGHT28 FINAL STUDENT REPLICATE BRR-FAY WEIGHT29 FINAL STUDENT REPLICATE BRR-FAY WEIGHT30 FINAL STUDENT REPLICATE BRR-FAY WEIGHT31 FINAL STUDENT REPLICATE BRR-FAY WEIGHT32 FINAL STUDENT REPLICATE BRR-FAY WEIGHT33 FINAL STUDENT REPLICATE BRR-FAY WEIGHT34 FINAL STUDENT REPLICATE BRR-FAY WEIGHT35 FINAL STUDENT REPLICATE BRR-FAY WEIGHT36 FINAL STUDENT REPLICATE BRR-FAY WEIGHT37 FINAL STUDENT REPLICATE BRR-FAY WEIGHT38 FINAL STUDENT REPLICATE BRR-FAY WEIGHT39 FINAL STUDENT REPLICATE BRR-FAY WEIGHT40 FINAL STUDENT REPLICATE BRR-FAY WEIGHT41 FINAL STUDENT REPLICATE BRR-FAY WEIGHT42 FINAL STUDENT REPLICATE BRR-FAY WEIGHT43 FINAL STUDENT REPLICATE BRR-FAY WEIGHT44 FINAL STUDENT REPLICATE BRR-FAY WEIGHT45 FINAL STUDENT REPLICATE BRR-FAY WEIGHT46 FINAL STUDENT REPLICATE BRR-FAY WEIGHT47 FINAL STUDENT REPLICATE BRR-FAY WEIGHT48 FINAL STUDENT REPLICATE BRR-FAY WEIGHT49 FINAL STUDENT REPLICATE BRR-FAY WEIGHT50 FINAL STUDENT REPLICATE BRR-FAY WEIGHT51 FINAL STUDENT REPLICATE BRR-FAY WEIGHT52 FINAL STUDENT REPLICATE BRR-FAY WEIGHT53 FINAL STUDENT REPLICATE BRR-FAY WEIGHT54 FINAL STUDENT REPLICATE BRR-FAY WEIGHT55 FINAL STUDENT REPLICATE BRR-FAY WEIGHT56 FINAL STUDENT REPLICATE BRR-FAY WEIGHT57 FINAL STUDENT REPLICATE BRR-FAY WEIGHT58 FINAL STUDENT REPLICATE BRR-FAY WEIGHT59 FINAL STUDENT REPLICATE BRR-FAY WEIGHT60 FINAL STUDENT REPLICATE BRR-FAY WEIGHT61 FINAL STUDENT REPLICATE BRR-FAY WEIGHT62 FINAL STUDENT REPLICATE BRR-FAY WEIGHT63 FINAL STUDENT REPLICATE BRR-FAY WEIGHT64 FINAL STUDENT REPLICATE BRR-FAY WEIGHT65 FINAL STUDENT REPLICATE BRR-FAY WEIGHT66 FINAL STUDENT REPLICATE BRR-FAY WEIGHT67 FINAL STUDENT REPLICATE BRR-FAY WEIGHT68 FINAL STUDENT REPLICATE BRR-FAY WEIGHT69 FINAL STUDENT REPLICATE BRR-FAY WEIGHT70 FINAL STUDENT REPLICATE BRR-FAY WEIGHT71 FINAL STUDENT REPLICATE BRR-FAY WEIGHT72 FINAL STUDENT REPLICATE BRR-FAY WEIGHT73 FINAL STUDENT REPLICATE BRR-FAY WEIGHT74 FINAL STUDENT REPLICATE BRR-FAY WEIGHT75 FINAL STUDENT REPLICATE BRR-FAY WEIGHT76 FINAL STUDENT REPLICATE BRR-FAY WEIGHT77 FINAL STUDENT REPLICATE BRR-FAY WEIGHT78 FINAL STUDENT REPLICATE BRR-FAY WEIGHT79 FINAL STUDENT REPLICATE BRR-FAY WEIGHT80 RANDOMIZED FINAL VARIANCE STRATUM (1-80) RANDOMLY ASSIGNED VARIANCE UNIT Senate weight - sum of weight within the country is 1000 Date of the database creation
    0 Albania 80000 ALB0006 Non-OECD Albania 1 1 10 1.0 2 1996 Female No 6.0 No, never No, never No, never None None 1.0 Yes Yes Yes Yes NaN NaN <ISCED level 3A> No No No No Other (e.g. home duties, retired) <ISCED level 3A> NaN NaN NaN NaN Working part-time <for pay> Country of test Country of test Country of test NaN Language of the test Yes No Yes No No No No Yes No Yes No Yes No Yes 8002 8001 8002 Two One None None None 0-10 books Agree Strongly agree Agree Agree Agree Agree Agree Strongly agree Disagree Agree Disagree Agree Agree Agree Not at all confident Not very confident Confident Confident Confident Not at all confident Confident Very confident Agree Disagree Agree Agree Agree Agree Agree Disagree Disagree Disagree Agree Disagree Disagree Agree NaN Disagree Likely Slightly likely Likely Likely Likely Very Likely Agree Agree Agree Agree Agree Agree Agree Agree Agree Courses after school Test Language Major in college Science Study harder Test Language Maximum classes Science Pursuing a career Math Often Sometimes Sometimes Sometimes Sometimes Never or rarely Never or rarely Never or rarely NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Every Lesson Every Lesson Every Lesson Every Lesson Every Lesson Never or Hardly Ever Most Lessons Never or Hardly Ever Every Lesson Most Lessons Every Lesson Every Lesson Every Lesson Never or Hardly Ever Most Lessons Every Lesson Every Lesson Every Lesson Always or almost always Sometimes Never or rarely Always or almost always Always or almost always Always or almost always Always or almost always Often Often Never or Hardly Ever Never or Hardly Ever Never or Hardly Ever Never or Hardly Ever Never or Hardly Ever Strongly disagree Strongly disagree Strongly disagree Strongly disagree Agree Agree Agree Strongly agree Strongly agree Disagree Agree Strongly disagree Disagree Agree Agree Strongly disagree Agree Agree Disagree Agree Agree Strongly disagree Strongly agree Strongly agree Strongly disagree Agree Strongly disagree Agree Agree Strongly agree Strongly disagree Strongly disagree Agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly disagree Disagree Strongly disagree Very much like me Very much like me Very much like me Somewhat like me Very much like me Somewhat like me Mostly like me Mostly like me Mostly like me Somewhat like me definitely do this definitely do this definitely do this definitely do this 4.0 2.0 1.0 1.0 1.0 2.0 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 99 99 99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN A Simple calculator 99 99 99 StQ Form B booklet 7 Standard set of booklets 16.17 0.0 Albania: Upper secondary education 0.32 -2.31 0.5206 -1.18 76.49 79.74 -1.3771 Albania Albania Albania 0.6994 NaN -0.48 1.85 NaN NaN NaN NaN 0.6400 NaN NaN 2.0 ISCED 3A, ISCED 4 -1.29 NaN ISCED 3A, ISCED 4 NaN -2.61 NaN NaN NaN NaN NaN -3.16 NaN Native NaN NaN NaN 0.80 0.91 A ISCED level 3 General NaN Albanian NaN NaN 0.6426 -0.77 -0.7332 0.2882 ISCED 3A, ISCED 4 NaN -0.9508 Building architects Primary school teachers 0.0521 NaN 12.0 -0.3407 Did not repeat a <grade> 0.41 NaN -1.04 -0.0455 1.3625 0.9374 0.4297 1.68 Albanian NaN NaN NaN -2.92 -1.8636 -0.6779 -0.7351 -0.7808 -0.0219 -0.1562 0.0486 -0.2199 -0.5983 -0.0807 -0.5901 -0.3346 406.8469 376.4683 344.5319 321.1637 381.9209 325.8374 324.2795 279.8800 267.4170 312.5954 409.1837 388.1524 373.3525 389.7102 415.4152 351.5423 375.6894 341.4161 386.5945 426.3203 396.7207 334.4057 328.9531 339.8582 354.6580 324.2795 345.3108 381.1419 380.3630 346.8687 319.6059 345.3108 360.8895 390.4892 322.7216 290.7852 345.3108 326.6163 407.6258 367.1210 249.5762 254.3420 406.8496 175.7053 218.5981 341.7009 408.8400 348.2283 367.8105 392.9877 8.9096 13.1249 13.0829 4.5315 13.0829 13.9235 13.1249 13.1249 4.3389 4.3313 13.7954 4.5315 4.3313 13.7954 13.9235 4.3389 4.3313 4.5084 4.5084 13.7954 4.5315 13.1249 13.0829 4.5315 13.0829 13.9235 13.1249 13.1249 4.3389 4.3313 13.7954 4.5315 4.3313 13.7954 13.9235 4.3389 4.3313 4.5084 4.5084 13.7954 4.5315 4.5084 4.5315 13.0829 4.5315 4.3313 4.5084 4.5084 13.7954 13.9235 4.3389 13.0829 13.9235 4.3389 4.3313 13.7954 13.9235 13.1249 13.1249 4.3389 13.0829 4.5084 4.5315 13.0829 4.5315 4.3313 4.5084 4.5084 13.7954 13.9235 4.3389 13.0829 13.9235 4.3389 4.3313 13.7954 13.9235 13.1249 13.1249 4.3389 13.0829 19 1 0.2098 22NOV13
    1 Albania 80000 ALB0006 Non-OECD Albania 1 2 10 1.0 2 1996 Female Yes, for more than one year 7.0 No, never No, never No, never One or two times None 1.0 Yes Yes NaN Yes NaN NaN <ISCED level 3A> Yes Yes No No Working full-time <for pay> <ISCED level 3A> No No No No Working full-time <for pay> Country of test Country of test Country of test NaN Language of the test Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes 8001 8001 8002 Three or more Three or more Three or more Two Two 201-500 books Disagree Strongly agree Disagree Disagree Agree Agree Disagree Disagree Strongly agree Strongly agree Disagree Agree Disagree Agree Confident Very confident Very confident Confident Very confident Confident Very confident Not very confident NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Strongly agree Strongly agree Strongly disagree Disagree Agree Disagree Likely Slightly likely Slightly likely Very Likely Slightly likely Likely Agree Agree Strongly agree Strongly agree Strongly agree Agree Agree Disagree Agree Courses after school Math Major in college Science Study harder Math Maximum classes Science Pursuing a career Science Sometimes Often Always or almost always Sometimes Always or almost always Never or rarely Never or rarely Often relating to known Improve understanding in my sleep Repeat examples I do not attend <out-of-school time lessons> i... 2 or more but less than 4 hours a week 2 or more but less than 4 hours a week Less than 2 hours a week NaN NaN 6.0 0.0 0.0 2.0 Rarely Rarely Frequently Sometimes Frequently Sometimes Frequently Never Frequently Know it well, understand the concept Know it well, understand the concept Heard of it once or twice Know it well, understand the concept Know it well, understand the concept Know it well, understand the concept Never heard of it Know it well, understand the concept Know it well, understand the concept Never heard of it Know it well, understand the concept Heard of it once or twice Know it well, understand the concept Know it well, understand the concept Never heard of it Heard of it often 45.0 45.0 45.0 7.0 6.0 2.0 NaN 30.0 Frequently Sometimes Frequently Frequently Sometimes Sometimes Sometimes Sometimes NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Not at all like me Not at all like me Mostly like me Somewhat like me Very much like me Somewhat like me Not much like me Not much like me Mostly like me Not much like me probably not do this probably do this probably not do this probably do this 1.0 2.0 3.0 2.0 2.0 3.0 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 99 99 99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN A Simple calculator 99 99 99 StQ Form A booklet 9 Standard set of booklets 16.17 0.0 Albania: Upper secondary education NaN NaN NaN NaN 15.35 23.47 NaN Albania Albania Albania NaN NaN 1.27 NaN NaN NaN -0.0681 0.7955 0.1524 0.6387 -0.08 2.0 ISCED 3A, ISCED 4 1.12 NaN ISCED 5A, 6 NaN 1.41 NaN NaN NaN NaN NaN 1.15 NaN Native NaN NaN NaN -0.39 0.00 A ISCED level 3 General NaN Albanian NaN 315.0 1.4702 0.34 -0.2514 0.6490 ISCED 5A, 6 270.0 NaN Tailors, dressmakers, furriers and hatters Building construction labourers -0.9492 8.0 16.0 1.3116 Did not repeat a <grade> NaN 90.0 NaN 0.6602 NaN NaN NaN NaN Albanian NaN NaN NaN 0.69 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 486.1427 464.3325 453.4273 472.9008 476.0165 325.6816 419.9330 378.6493 359.9548 384.1019 373.1968 444.0801 456.5431 401.2385 461.2167 366.9653 459.6588 426.1645 423.0488 443.3011 389.5544 438.6275 417.5962 379.4283 438.6275 440.1854 456.5431 486.9216 458.1010 444.0801 411.3647 437.8486 457.3220 454.2063 460.4378 434.7328 448.7537 494.7110 429.2803 434.7328 406.2936 349.8975 400.7334 369.7553 396.7618 548.9929 471.5964 471.5964 443.6218 454.8116 8.9096 13.1249 13.0829 4.5315 13.0829 13.9235 13.1249 13.1249 4.3389 4.3313 13.7954 4.5315 4.3313 13.7954 13.9235 4.3389 4.3313 4.5084 4.5084 13.7954 4.5315 13.1249 13.0829 4.5315 13.0829 13.9235 13.1249 13.1249 4.3389 4.3313 13.7954 4.5315 4.3313 13.7954 13.9235 4.3389 4.3313 4.5084 4.5084 13.7954 4.5315 4.5084 4.5315 13.0829 4.5315 4.3313 4.5084 4.5084 13.7954 13.9235 4.3389 13.0829 13.9235 4.3389 4.3313 13.7954 13.9235 13.1249 13.1249 4.3389 13.0829 4.5084 4.5315 13.0829 4.5315 4.3313 4.5084 4.5084 13.7954 13.9235 4.3389 13.0829 13.9235 4.3389 4.3313 13.7954 13.9235 13.1249 13.1249 4.3389 13.0829 19 1 0.2098 22NOV13
    2 Albania 80000 ALB0006 Non-OECD Albania 1 3 9 1.0 9 1996 Female Yes, for more than one year 6.0 No, never No, never No, never None None 1.0 Yes Yes No Yes No No <ISCED level 3B, 3C> Yes Yes Yes No Working full-time <for pay> <ISCED level 3A> Yes No Yes Yes Working full-time <for pay> Country of test Country of test Country of test NaN Language of the test Yes Yes Yes Yes No Yes Yes Yes Yes Yes No Yes No Yes 8001 8001 8001 Three or more Two Two One Two More than 500 books Agree Strongly agree Agree Agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Agree Strongly agree Strongly agree Agree Confident Very confident Very confident Confident Very confident Not very confident Very confident Confident NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Strongly agree Agree Strongly agree Strongly disagree Strongly agree Strongly disagree Likely Likely Very Likely Very Likely Very Likely Slightly likely Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Agree Strongly agree Strongly agree Strongly agree Courses after school Math Major in college Science Study harder Math Maximum classes Science Pursuing a career Science Sometimes Always or almost always Sometimes Never or rarely Always or almost always Never or rarely Never or rarely Never or rarely Most important Improve understanding learning goals more information Less than 2 hours a week 2 or more but less than 4 hours a week 4 or more but less than 6 hours a week I do not attend <out-of-school time lessons> i... NaN 6.0 6.0 7.0 2.0 3.0 Frequently Sometimes Frequently Rarely Frequently Rarely Frequently Sometimes Frequently Never heard of it Know it well, understand the concept Heard of it once or twice Know it well, understand the concept Know it well, understand the concept Know it well, understand the concept Heard of it once or twice Know it well, understand the concept Know it well, understand the concept Heard of it once or twice Know it well, understand the concept Know it well, understand the concept Know it well, understand the concept Know it well, understand the concept Know it well, understand the concept Know it well, understand the concept 60.0 NaN NaN 5.0 4.0 2.0 24.0 30.0 Frequently Frequently Frequently Frequently Frequently Frequently Rarely Rarely NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Not much like me Not much like me Very much like me Very much like me Somewhat like me Mostly like me Mostly like me Very much like me Mostly like me Very much like me probably not do this definitely do this definitely not do this probably do this 1.0 3.0 4.0 1.0 3.0 4.0 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 99 99 99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN A Simple calculator 99 99 99 StQ Form A booklet 3 Standard set of booklets 15.58 -1.0 Albania: Lower secondary education NaN NaN NaN NaN 22.57 NaN NaN Albania Albania Albania NaN NaN 1.27 NaN NaN NaN 0.5359 0.7955 1.2219 0.8215 -0.89 2.0 ISCED 5A, 6 -0.69 NaN ISCED 5A, 6 NaN 0.14 NaN NaN NaN NaN NaN -0.40 NaN Native NaN NaN NaN 1.59 1.23 A ISCED level 2 General NaN Albanian NaN 300.0 0.9618 0.34 -0.2514 2.0389 ISCED 5A, 6 NaN NaN Housewife Bricklayers and related workers 0.9383 24.0 16.0 0.9918 Did not repeat a <grade> NaN NaN NaN 2.2350 NaN NaN NaN NaN Albanian NaN NaN NaN -0.23 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 533.2684 481.0796 489.6479 490.4269 533.2684 611.1622 486.5322 567.5417 541.0578 544.9525 597.1413 495.1005 576.8889 507.5635 556.6365 594.8045 473.2902 554.2997 537.1631 568.3206 471.7324 431.2276 460.8272 419.5435 456.9325 559.7523 501.3320 555.0787 467.0587 506.7845 580.7836 481.0796 555.0787 453.8168 491.2058 527.0369 444.4695 516.1318 403.9648 476.4060 401.2100 404.3872 387.7067 431.3938 401.2100 499.6643 428.7952 492.2044 512.7191 499.6643 8.4871 12.7307 12.7307 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 12.7307 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 12.7307 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 4.2436 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 4.2436 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 12.7307 19 1 0.1999 22NOV13
    3 Albania 80000 ALB0006 Non-OECD Albania 1 4 9 1.0 8 1996 Female Yes, for more than one year 6.0 No, never No, never No, never None None 1.0 Yes Yes No Yes No No <ISCED level 3B, 3C> No No No No Working full-time <for pay> <ISCED level 3A> Yes Yes No No Working full-time <for pay> Country of test Country of test Country of test NaN Language of the test Yes Yes Yes Yes Yes Yes Yes Yes Yes Yes No Yes Yes No 8001 8001 8002 Three or more Two One None One 11-25 books NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Strongly agree Disagree Agree Agree Disagree Strongly agree Disagree Agree Agree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN relating to known new ways learning goals more information I do not attend <out-of-school time lessons> i... I do not attend <out-of-school time lessons> i... Less than 2 hours a week I do not attend <out-of-school time lessons> i... 10.0 2.0 2.0 0.0 0.0 3.0 Sometimes Sometimes Sometimes Sometimes Frequently Sometimes Frequently Rarely Frequently Heard of it often Heard of it often Heard of it often Know it well, understand the concept Heard of it a few times Know it well, understand the concept Never heard of it Know it well, understand the concept Know it well, understand the concept Never heard of it Know it well, understand the concept Never heard of it Know it well, understand the concept Know it well, understand the concept Heard of it often Heard of it often 45.0 45.0 45.0 3.0 3.0 2.0 NaN 28.0 Frequently Sometimes Frequently Rarely Frequently Frequently Sometimes Sometimes Every Lesson Every Lesson Every Lesson Every Lesson Every Lesson NaN Every Lesson Every Lesson Every Lesson Every Lesson Every Lesson Every Lesson Every Lesson Every Lesson Never or Hardly Ever Most Lessons Every Lesson Every Lesson Always or almost always NaN NaN NaN NaN NaN NaN NaN Never or rarely Never or Hardly Ever Never or Hardly Ever Never or Hardly Ever Never or Hardly Ever NaN NaN NaN Strongly agree Strongly agree Strongly agree Strongly agree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 99 99 99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 99 99 99 StQ Form C booklet 2 Standard set of booklets 15.67 -1.0 Albania: Lower secondary education 0.31 NaN NaN NaN 14.21 NaN NaN Albania Albania Albania -0.3788 NaN 1.27 1.80 NaN NaN 0.3220 0.7955 NaN 0.7266 0.24 2.0 ISCED 5A, 6 0.04 NaN ISCED 5A, 6 NaN -0.73 NaN NaN NaN NaN NaN -0.40 NaN Native NaN NaN NaN NaN NaN A ISCED level 2 General NaN Albanian NaN 135.0 NaN NaN NaN NaN ISCED 3B, C 135.0 1.6748 Housewife Cleaners and helpers in offices, hotels and ot... NaN 17.0 16.0 NaN Did not repeat a <grade> 0.18 90.0 NaN NaN 0.7644 3.3108 2.3916 1.68 Albanian NaN NaN NaN -1.17 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 412.2215 498.6836 415.3373 466.7472 454.2842 538.4094 511.9255 553.9882 483.8838 479.2102 525.1675 529.0622 539.1883 516.5992 501.7993 658.3658 567.2301 669.2709 652.1343 645.1239 508.0308 522.0517 524.3885 495.5678 458.1788 524.3885 462.0735 494.0100 459.7367 471.4208 534.5147 455.8420 504.1362 454.2842 483.8838 521.2728 481.5470 503.3572 469.8629 478.4312 547.3630 481.4353 461.5776 425.0393 471.9036 438.6796 481.5740 448.9370 474.1141 426.5573 8.4871 12.7307 12.7307 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 12.7307 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 12.7307 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 4.2436 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 4.2436 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 12.7307 19 1 0.1999 22NOV13
    4 Albania 80000 ALB0006 Non-OECD Albania 1 5 9 1.0 10 1996 Female Yes, for more than one year 6.0 No, never No, never No, never One or two times None 2.0 Yes Yes Yes NaN NaN NaN She did not complete <ISCED level 1> No No No No Working part-time <for pay> <ISCED level 3B, 3C> No No No Yes Working part-time <for pay> Country of test Country of test Country of test NaN Language of the test Yes Yes No Yes Yes Yes Yes Yes Yes Yes No Yes Yes Yes 8001 8002 8001 Two One Two None One 101-200 books Disagree Strongly agree Disagree Disagree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Agree Confident Very confident NaN Very confident Very confident Confident Very confident Not very confident Strongly agree Strongly agree Agree Strongly agree Strongly agree Disagree Disagree Disagree Agree Agree Strongly agree Strongly agree Disagree Disagree Strongly agree Disagree Likely Likely Likely Likely Slightly likely Very Likely Strongly agree Strongly agree Agree Strongly agree Strongly agree Agree Strongly agree Strongly agree Strongly agree Courses after school Test Language Major in college Math Study harder Math Maximum classes Math Pursuing a career Math Always or almost always Always or almost always Often Often Sometimes NaN Sometimes Sometimes NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Every Lesson Most Lessons Every Lesson Most Lessons Some Lessons Some Lessons Some Lessons Most Lessons Some Lessons Most Lessons Every Lesson Most Lessons Every Lesson Some Lessons Most Lessons Most Lessons Every Lesson Most Lessons Always or almost always Often Sometimes Often Often Often Always or almost always Often Often Some Lessons Some Lessons NaN Most Lessons Never or Hardly Ever Strongly disagree Disagree Strongly agree Strongly agree Agree Strongly agree Agree Disagree Strongly agree Disagree Agree Agree Strongly agree Agree Agree Agree Agree Agree Agree Strongly disagree Strongly agree Strongly agree Strongly disagree Strongly agree Strongly disagree Strongly agree Strongly agree Strongly agree Disagree Strongly disagree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Agree Strongly agree Strongly disagree Agree Strongly agree Disagree NaN Mostly like me Very much like me Very much like me Very much like me Very much like me Very much like me Mostly like me Very much like me Mostly like me definitely do this definitely do this definitely do this definitely do this 1.0 2.0 1.0 2.0 1.0 2.0 1.0 1.0 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 99 99 99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 99 99 99 StQ Form B booklet 4 Standard set of booklets 15.50 -1.0 Albania: Lower secondary education 1.02 1.38 1.2115 2.63 80.92 NaN -0.0784 Albania Albania Albania 0.5403 NaN 1.27 -0.08 NaN NaN NaN NaN 0.6400 NaN NaN 2.0 ISCED 3A, ISCED 4 -0.69 NaN ISCED 3A, ISCED 4 NaN -0.57 NaN NaN NaN NaN NaN 0.24 NaN Native NaN NaN NaN 1.59 0.30 A ISCED level 2 General NaN Albanian NaN NaN 1.8169 0.41 0.6584 1.6881 None NaN 0.6709 Housewife Economists 1.2387 NaN 12.0 1.0819 Did not repeat a <grade> -0.06 NaN -0.02 2.8039 0.7644 0.9374 0.4297 0.11 Albanian NaN NaN NaN -1.17 0.6517 0.4908 0.8675 0.0505 0.4940 0.9986 0.0486 0.9341 0.4052 0.0358 0.2492 1.2260 381.9209 328.1742 403.7311 418.5309 395.1628 373.3525 293.1220 364.0053 430.2150 403.7311 414.6362 385.8155 392.8260 448.9095 474.6144 417.7520 353.1002 424.7624 457.4778 459.0357 339.0793 309.4797 340.6372 369.4579 384.2577 373.3525 392.0470 347.6476 342.1950 342.1950 432.5518 431.7729 399.0575 369.4579 341.4161 297.0167 353.8791 347.6476 314.1533 311.0375 311.7707 141.7883 293.5015 272.8495 260.1405 361.5628 275.7740 372.7527 403.5248 422.1746 8.4871 12.7307 12.7307 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 12.7307 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 12.7307 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 4.2436 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 12.7307 4.2436 4.2436 12.7307 4.2436 4.2436 4.2436 4.2436 12.7307 12.7307 4.2436 12.7307 12.7307 4.2436 4.2436 12.7307 12.7307 12.7307 12.7307 4.2436 12.7307 19 1 0.1999 22NOV13
    In [33]:
    # check the number of countries particpating in the PISA survey
    raw_data['Country code 3-character'].nunique()
    
    Out[33]:
    68

    we observe that not all countries are accounted for

    In [34]:
    # extract the 3 alphabet Country code for further use 
    raw_data['Stratum ID 7-character (cnt + region ID + original stratum ID)']= raw_data['Stratum ID 7-character (cnt + region ID + original stratum ID)'].str[0:3]
    
    In [35]:
    # view al columns
    pd.options.display.max_columns = None
    raw_data.sample(5)
    
    Out[35]:
    x Country code 3-character Adjudicated sub-region code 7-digit code (3-digit country code + region ID + stratum ID) Stratum ID 7-character (cnt + region ID + original stratum ID) OECD country National Centre 6-digit Code School ID 7-digit (region ID + stratum ID + 3-digit school ID) Student ID International Grade National Study Programme Birth - Month Birth -Year Gender Attend <ISCED 0> Age at <ISCED 1> Repeat - <ISCED 1> Repeat - <ISCED 2> Repeat - <ISCED 3> Truancy - Late for School Truancy - Skip whole school day Truancy - Skip classes within school day At Home - Mother At Home - Father At Home - Brothers At Home - Sisters At Home - Grandparents At Home - Others Mother<Highest Schooling> Mother Qualifications - <ISCED level 6> Mother Qualifications - <ISCED level 5A> Mother Qualifications - <ISCED level 5B> Mother Qualifications - <ISCED level 4> Mother Current Job Status Father<Highest Schooling> Father Qualifications - <ISCED level 6> Father Qualifications - <ISCED level 5A> Father Qualifications - <ISCED level 5B> Father Qualifications - <ISCED level 4> Father Current Job Status Country of Birth International - Self Country of Birth International - Mother Country of Birth International - Father Age of arrival in <country of test> International Language at Home Possessions - desk Possessions - own room Possessions - study place Possessions - computer Possessions - software Possessions - Internet Possessions - literature Possessions - poetry Possessions - art Possessions - textbooks Possessions - <technical reference books> Possessions - dictionary Possessions - dishwasher Possessions - <DVD> Possessions - <Country item 1> Possessions - <Country item 2> Possessions - <Country item 3> How many - cellular phones How many - televisions How many - computers How many - cars How many - rooms bath or shower How many books at home Math Interest - Enjoy Reading Instrumental Motivation - Worthwhile for Work Math Interest - Look Forward to Lessons Math Interest - Enjoy Maths Instrumental Motivation - Worthwhile for Career Chances Math Interest - Interested Instrumental Motivation - Important for Future Study Instrumental Motivation - Helps to Get a Job Subjective Norms -Friends Do Well in Mathematics Subjective Norms -Friends Work Hard on Mathematics Subjective Norms - Friends Enjoy Mathematics Tests Subjective Norms - Parents Believe Studying Mathematics Is Important Subjective Norms - Parents Believe Mathematics Is Important for Career Subjective Norms - Parents Like Mathematics Math Self-Efficacy - Using a <Train Timetable> Math Self-Efficacy - Calculating TV Discount Math Self-Efficacy - Calculating Square Metres of Tiles Math Self-Efficacy - Understanding Graphs in Newspapers Math Self-Efficacy - Solving Equation 1 Math Self-Efficacy - Distance to Scale Math Self-Efficacy - Solving Equation 2 Math Self-Efficacy - Calculate Petrol Consumption Rate Math Anxiety - Worry That It Will Be Difficult Math Self-Concept - Not Good at Maths Math Anxiety - Get Very Tense Math Self-Concept- Get Good <Grades> Math Anxiety - Get Very Nervous Math Self-Concept - Learn Quickly Math Self-Concept - One of Best Subjects Math Anxiety - Feel Helpless Math Self-Concept - Understand Difficult Work Math Anxiety - Worry About Getting Poor <Grades> Perceived Control - Can Succeed with Enough Effort Perceived Control - Doing Well is Completely Up to Me Perceived Control - Family Demands and Problems Perceived Control - Different Teachers Perceived Control - If I Wanted I Could Perform Well Perceived Control - Perform Poorly Regardless Attributions to Failure - Not Good at Maths Problems Attributions to Failure - Teacher Did Not Explain Well Attributions to Failure - Bad Guesses Attributions to Failure - Material Too Hard Attributions to Failure - Teacher Didnt Get Students Interested Attributions to Failure - Unlucky Math Work Ethic - Homework Completed in Time Math Work Ethic - Work Hard on Homework Math Work Ethic - Prepared for Exams Math Work Ethic - Study Hard for Quizzes Math Work Ethic - Study Until I Understand Everything Math Work Ethic - Pay Attention in Classes Math Work Ethic - Listen in Classes Math Work Ethic - Avoid Distractions When Studying Math Work Ethic - Keep Work Organized Math Intentions - Mathematics vs. Language Courses After School Math Intentions - Mathematics vs. Science Related Major in College Math Intentions - Study Harder in Mathematics vs. Language Classes Math Intentions - Take Maximum Number of Mathematics vs. Science Classes Math Intentions - Pursuing a Career That Involves Mathematics vs. Science Math Behaviour - Talk about Maths with Friends Math Behaviour - Help Friends with Maths Math Behaviour - <Extracurricular> Activity Math Behaviour - Participate in Competitions Math Behaviour - Study More Than 2 Extra Hours a Day Math Behaviour - Play Chess Math Behaviour - Computer programming Math Behaviour - Participate in Math Club Learning Strategies- Important Parts vs. Existing Knowledge vs. Learn by Heart Learning Strategies- Improve Understanding vs. New Ways vs. Memory Learning Strategies - Other Subjects vs. Learning Goals vs. Rehearse Problems Learning Strategies - Repeat Examples vs. Everyday Applications vs. More Information Out of school lessons - <test lang> Out of school lessons - <maths> Out of school lessons - <science> Out of school lessons - other Out-of-School Study Time - Homework Out-of-School Study Time - Guided Homework Out-of-School Study Time - Personal Tutor Out-of-School Study Time - Commercial Company Out-of-School Study Time - With Parent Out-of-School Study Time - Computer Experience with Applied Maths Tasks - Use <Train Timetable> Experience with Applied Maths Tasks - Calculate Price including Tax Experience with Applied Maths Tasks - Calculate Square Metres Experience with Applied Maths Tasks - Understand Scientific Tables Experience with Pure Maths Tasks - Solve Equation 1 Experience with Applied Maths Tasks - Use a Map to Calculate Distance Experience with Pure Maths Tasks - Solve Equation 2 Experience with Applied Maths Tasks - Calculate Power Consumption Rate Experience with Applied Maths Tasks - Solve Equation 3 Familiarity with Math Concepts - Exponential Function Familiarity with Math Concepts - Divisor Familiarity with Math Concepts - Quadratic Function Overclaiming - Proper Number Familiarity with Math Concepts - Linear Equation Familiarity with Math Concepts - Vectors Familiarity with Math Concepts - Complex Number Familiarity with Math Concepts - Rational Number Familiarity with Math Concepts - Radicals Overclaiming - Subjunctive Scaling Familiarity with Math Concepts - Polygon Overclaiming - Declarative Fraction Familiarity with Math Concepts - Congruent Figure Familiarity with Math Concepts - Cosine Familiarity with Math Concepts - Arithmetic Mean Familiarity with Math Concepts - Probability Min in <class period> - <test lang> Min in <class period> - <Maths> Min in <class period> - <Science> No of <class period> p/wk - <test lang> No of <class period> p/wk - <Maths> No of <class period> p/wk - <Science> No of ALL <class period> a week Class Size - No of Students in <Test Language> Class OTL - Algebraic Word Problem in Math Lesson OTL - Algebraic Word Problem in Tests OTL - Procedural Task in Math Lesson OTL - Procedural Task in Tests OTL - Pure Math Reasoning in Math Lesson OTL - Pure Math Reasoning in Tests OTL - Applied Math Reasoning in Math Lesson OTL - Applied Math Reasoning in Tests Math Teaching - Teacher shows interest Math Teaching - Extra help Math Teaching - Teacher helps Math Teaching - Teacher continues Math Teaching - Express opinions Teacher-Directed Instruction - Sets Clear Goals Teacher-Directed Instruction - Encourages Thinking and Reasoning Student Orientation - Differentiates Between Students When Giving Tasks Student Orientation - Assigns Complex Projects Formative Assessment - Gives Feedback Teacher-Directed Instruction - Checks Understanding Student Orientation - Has Students Work in Small Groups Teacher-Directed Instruction - Summarizes Previous Lessons Student Orientation - Plans Classroom Activities Formative Assessment - Gives Feedback on Strengths and Weaknesses Formative Assessment - Informs about Expectations Teacher-Directed Instruction - Informs about Learning Goals Formative Assessment - Tells How to Get Better Cognitive Activation - Teacher Encourages to Reflect Problems Cognitive Activation - Gives Problems that Require to Think Cognitive Activation - Asks to Use Own Procedures Cognitive Activation - Presents Problems with No Obvious Solutions Cognitive Activation - Presents Problems in Different Contexts Cognitive Activation - Helps Learn from Mistakes Cognitive Activation - Asks for Explanations Cognitive Activation - Apply What We Learned Cognitive Activation - Problems with Multiple Solutions Disciplinary Climate - Students DonÂ’t Listen Disciplinary Climate - Noise and Disorder Disciplinary Climate - Teacher Has to Wait Until its Quiet Disciplinary Climate - Students DonÂ’t Work Well Disciplinary Climate - Students Start Working Late Vignette Teacher Support -Homework Every Other Day/Back in Time Vignette Teacher Support - Homework Once a Week/Back in Time Vignette Teacher Support - Homework Once a Week/Not Back in Time Teacher Support - Lets Us Know We Have to Work Hard Teacher Support - Provides Extra Help When Needed Teacher Support - Helps Students with Learning Teacher Support - Gives Opportunity to Express Opinions Vignette Classroom Management - Students Frequently Interrupt/Teacher Arrives Early Vignette Classroom Management - Students Are Calm/Teacher Arrives on Time Vignette Classroom Management - Students Frequently Interrupt/Teacher Arrives Late Classroom Management - Students Listen Classroom Management - Teacher Keeps Class Orderly Classroom Management - Teacher Starts On Time Classroom Management - Wait Long to <Quiet Down> Student-Teacher Relation - Get Along with Teachers Student-Teacher Relation - Teachers Are Interested Student-Teacher Relation - Teachers Listen to Students Student-Teacher Relation - Teachers Help Students Student-Teacher Relation - Teachers Treat Students Fair Sense of Belonging - Feel Like Outsider Sense of Belonging - Make Friends Easily Sense of Belonging - Belong at School Sense of Belonging - Feel Awkward at School Sense of Belonging - Liked by Other Students Sense of Belonging - Feel Lonely at School Sense of Belonging - Feel Happy at School Sense of Belonging - Things Are Ideal at School Sense of Belonging - Satisfied at School Attitude towards School - Does Little to Prepare Me for Life Attitude towards School - Waste of Time Attitude towards School - Gave Me Confidence Attitude towards School- Useful for Job Attitude toward School - Helps to Get a Job Attitude toward School - Prepare for College Attitude toward School - Enjoy Good Grades Attitude toward School - Trying Hard is Important Perceived Control - Can Succeed with Enough Effort Perceived Control - My Choice Whether I Will Be Good Perceived Control - Problems Prevent from Putting Effort into School Perceived Control - Different Teachers Would Make Me Try Harder Perceived Control - Could Perform Well if I Wanted Perceived Control - Perform Poor Regardless Perseverance - Give up easily Perseverance - Put off difficult problems Perseverance - Remain interested Perseverance - Continue to perfection Perseverance - Exceed expectations Openness for Problem Solving - Can Handle a Lot of Information Openness for Problem Solving - Quick to Understand Openness for Problem Solving - Seek Explanations Openness for Problem Solving - Can Link Facts Openness for Problem Solving - Like to Solve Complex Problems Problem Text Message - Press every button Problem Text Message - Trace steps Problem Text Message - Manual Problem Text Message - Ask a friend Problem Route Selection - Read brochure Problem Route Selection - Study map Problem Route Selection - Leave it to brother Problem Route Selection - Just drive Problem Ticket Machine - Similarities Problem Ticket Machine - Try buttons Problem Ticket Machine - Ask for help Problem Ticket Machine - Find ticket office At Home - Desktop Computer At Home - Portable laptop At Home - Tablet computer At Home - Internet connection At Home - Video games console At Home - Cell phone w/o Internet At Home - Cell phone with Internet At Home - Mp3/Mp4 player At Home - Printer At Home - USB (memory) stick At Home - Ebook reader At school - Desktop Computer At school - Portable laptop At school - Tablet computer At school - Internet connection At school - Printer At school - USB (memory) stick At school - Ebook reader First use of computers First access to Internet Internet at School Internet out-of-school - Weekday Internet out-of-school - Weekend Out-of-school 8 - One player games. Out-of-school 8 - ColLabourative games. Out-of-school 8 - Use email Out-of-school 8 - Chat on line Out-of-school 8 - Social networks Out-of-school 8 - Browse the Internet for fun Out-of-school 8 - Read news Out-of-school 8 - Obtain practical information from the Internet Out-of-school 8 - Download music Out-of-school 8 - Upload content Out-of-school 9 - Internet for school Out-of-school 9 - Email students Out-of-school 9 - Email teachers Out-of-school 9 - Download from School Out-of-school 9 - Announcements Out-of-school 9 - Homework Out-of-school 9 - Share school material At School - Chat on line At School - Email At School - Browse for schoolwork At School - Download from website At School - Post on website At School - Simulations At School - Practice and drilling At School - Homework At School - Group work Maths lessons - Draw graph Maths lessons - Calculation with numbers Maths lessons - Geometric figures Maths lessons - Spreadsheet Maths lessons - Algebra Maths lessons - Histograms Maths lessons - Change in graphs Attitudes - Useful for schoolwork Attitudes - Homework more fun Attitudes - Source of information Attitudes - Troublesome Attitudes - Not suitable for schoolwork Attitudes - Too unreliable Miss 2 months of <ISCED 1> Miss 2 months of <ISCED 2> Future Orientation - Internship Future Orientation - Work-site visits Future Orientation - Job fair Future Orientation - Career advisor at school Future Orientation - Career advisor outside school Future Orientation - Questionnaire Future Orientation - Internet search Future Orientation - Tour<ISCED 3-5> institution Future Orientation - web search <ISCED 3-5> prog Future Orientation - <country specific item> Acquired skills - Find job info - Yes, at school Acquired skills - Find job info - Yes, out of school Acquired skills - Find job info - No, never Acquired skills - Search for job - Yes, at school Acquired skills - Search for job - Yes, out of school Acquired skills - Search for job - No, never Acquired skills - Write resume - Yes, at school Acquired skills - Write resume - Yes, out of school Acquired skills - Write resume - No, never Acquired skills - Job interview - Yes, at school Acquired skills - Job interview - Yes, out of school Acquired skills - Job interview - No, never Acquired skills - ISCED 3-5 programs - Yes, at school Acquired skills - ISCED 3-5 programs - Yes, out of school Acquired skills - ISCED 3-5 programs - No, never Acquired skills - Student financing - Yes, at school Acquired skills - Student financing - Yes, out of school Acquired skills - Student financing - No, never First language learned Age started learning <test language> Language spoken - Mother Language spoken - Father Language spoken - Siblings Language spoken - Best friend Language spoken - Schoolmates Activities language - Reading Activities language - Watching TV Activities language - Internet surfing Activities language - Writing emails Types of support <test language> - remedial lessons Amount of support <test language> Attend lessons <heritage language> - focused Attend lessons <heritage language> - school subjects Instruction in <heritage language> Acculturation - Mother Immigrant (Filter) Acculturation - Enjoy <Host Culture> Friends Acculturation - Enjoy <Heritage Culture> Friends Acculturation - Enjoy <Host Culture> Celebrations Acculturation - Enjoy <Heritage Culture> Celebrations Acculturation - Spend Time with <Host Culture> Friends Acculturation - Spend Time with <Heritage Culture> Friends Acculturation - Participate in <Host Culture> Celebrations Acculturation - Participate in <Heritage Culture> Celebrations Acculturation - Perceived Host-Heritage Cultural Differences - Values Acculturation - Perceived Host-Heritage Cultural Differences - Mother Treatment Acculturation - Perceived Host-Heritage Cultural Differences - Teacher Treatment Calculator Use Effort-real 1 Effort-real 2 Difference in Effort Student Questionnaire Form Booklet ID Standard or simplified set of booklets Age of student Grade compared to modal grade in country Unique national study programme code Mathematics Anxiety Attitude towards School: Learning Outcomes Attitude towards School: Learning Activities Sense of Belonging to School Father SQ ISEI Mother SQ ISEI Mathematics Teacher's Classroom Management Country of Birth National Categories- Father Country of Birth National Categories- Mother Country of Birth National Categories- Self Cognitive Activation in Mathematics Lessons Cultural Distance between Host and Heritage Culture Cultural Possessions Disciplinary Climate ICT Entertainment Use Index of economic, social and cultural status Experience with Applied Mathematics Tasks at School Experience with Pure Mathematics Tasks at School Attributions to Failure in Mathematics Familiarity with Mathematical Concepts Familiarity with Mathematical Concepts (Signal Detection Adjusted) Family Structure Educational level of father (ISCED) Home educational resources Acculturation: Heritage Culture Oriented Strategies Highest educational level of parents Highest parental occupational status Home Possessions ICT Use at Home for School-related Tasks Acculturation: Host Culture Oriented Strategies Attitudes Towards Computers: Limitations of the Computer as a Tool for School Learning Attitudes Towards Computers: Computer as a Tool for School Learning ICT Availability at Home ICT resources ICT Availability at School Immigration status Information about Careers Information about the Labour Market provided by the School Information about the Labour Market provided outside of School Instrumental Motivation for Mathematics Mathematics Interest ISCED designation ISCED level ISCED orientation Preference for Heritage Language in Conversations with Family and Friends Language at home (3-digit code) Preference for Heritage Language in Language Reception and Production Learning time (minutes per week) - <test language> Mathematics Behaviour Mathematics Self-Efficacy Mathematics Intentions Mathematics Work Ethic Educational level of mother (ISCED) Learning time (minutes per week)- <Mathematics> Mathematics Teacher's Support ISCO-08 Occupation code - Mother ISCO-08 Occupation code - Father Openness for Problem Solving Out-of-School Study Time Highest parental education in years Perseverance Grade Repetition Mathematics Self-Concept Learning time (minutes per week) - <Science> Teacher Student Relations Subjective Norms in Mathematics Teacher Behaviour: Formative Assessment Teacher Behaviour: Student Orientation Teacher Behaviour: Teacher-directed Instruction Teacher Support Language of the test Time of computer use (mins) Use of ICT in Mathematic Lessons Use of ICT at School Wealth Attitude towards School: Learning Outcomes (Anchored) Attitude towards School: Learning Activities (Anchored) Sense of Belonging to School (Anchored) Mathematics Teacher's Classroom Management (Anchored) Cognitive Activation in Mathematics Lessons (Anchored) Instrumental Motivation for Mathematics (Anchored) Mathematics Interest (Anchored) Mathematics Work Ethic (Anchored) Mathematics Teacher's Support (Anchored) Mathematics Self-Concept (Anchored) Teacher Student Relations (Anchored) Subjective Norms in Mathematics (Anchored) Plausible value 1 in mathematics Plausible value 2 in mathematics Plausible value 3 in mathematics Plausible value 4 in mathematics Plausible value 5 in mathematics Plausible value 1 in content subscale of math - Change and Relationships Plausible value 2 in content subscale of math - Change and Relationships Plausible value 3 in content subscale of math - Change and Relationships Plausible value 4 in content subscale of math - Change and Relationships Plausible value 5 in content subscale of math - Change and Relationships Plausible value 1 in content subscale of math - Quantity Plausible value 2 in content subscale of math - Quantity Plausible value 3 in content subscale of math - Quantity Plausible value 4 in content subscale of math - Quantity Plausible value 5 in content subscale of math - Quantity Plausible value 1 in content subscale of math - Space and Shape Plausible value 2 in content subscale of math - Space and Shape Plausible value 3 in content subscale of math - Space and Shape Plausible value 4 in content subscale of math - Space and Shape Plausible value 5 in content subscale of math - Space and Shape Plausible value 1 in content subscale of math - Uncertainty and Data Plausible value 2 in content subscale of math - Uncertainty and Data Plausible value 3 in content subscale of math - Uncertainty and Data Plausible value 4 in content subscale of math - Uncertainty and Data Plausible value 5 in content subscale of math - Uncertainty and Data Plausible value 1 in process subscale of math - Employ Plausible value 2 in process subscale of math - Employ Plausible value 3 in process subscale of math - Employ Plausible value 4 in process subscale of math - Employ Plausible value 5 in process subscale of math - Employ Plausible value 1 in process subscale of math - Formulate Plausible value 2 in process subscale of math - Formulate Plausible value 3 in process subscale of math - Formulate Plausible value 4 in process subscale of math - Formulate Plausible value 5 in process subscale of math - Formulate Plausible value 1 in process subscale of math - Interpret Plausible value 2 in process subscale of math - Interpret Plausible value 3 in process subscale of math - Interpret Plausible value 4 in process subscale of math - Interpret Plausible value 5 in process subscale of math - Interpret Plausible value 1 in reading Plausible value 2 in reading Plausible value 3 in reading Plausible value 4 in reading Plausible value 5 in reading Plausible value 1 in science Plausible value 2 in science Plausible value 3 in science Plausible value 4 in science Plausible value 5 in science FINAL STUDENT WEIGHT FINAL STUDENT REPLICATE BRR-FAY WEIGHT1 FINAL STUDENT REPLICATE BRR-FAY WEIGHT2 FINAL STUDENT REPLICATE BRR-FAY WEIGHT3 FINAL STUDENT REPLICATE BRR-FAY WEIGHT4 FINAL STUDENT REPLICATE BRR-FAY WEIGHT5 FINAL STUDENT REPLICATE BRR-FAY WEIGHT6 FINAL STUDENT REPLICATE BRR-FAY WEIGHT7 FINAL STUDENT REPLICATE BRR-FAY WEIGHT8 FINAL STUDENT REPLICATE BRR-FAY WEIGHT9 FINAL STUDENT REPLICATE BRR-FAY WEIGHT10 FINAL STUDENT REPLICATE BRR-FAY WEIGHT11 FINAL STUDENT REPLICATE BRR-FAY WEIGHT12 FINAL STUDENT REPLICATE BRR-FAY WEIGHT13 FINAL STUDENT REPLICATE BRR-FAY WEIGHT14 FINAL STUDENT REPLICATE BRR-FAY WEIGHT15 FINAL STUDENT REPLICATE BRR-FAY WEIGHT16 FINAL STUDENT REPLICATE BRR-FAY WEIGHT17 FINAL STUDENT REPLICATE BRR-FAY WEIGHT18 FINAL STUDENT REPLICATE BRR-FAY WEIGHT19 FINAL STUDENT REPLICATE BRR-FAY WEIGHT20 FINAL STUDENT REPLICATE BRR-FAY WEIGHT21 FINAL STUDENT REPLICATE BRR-FAY WEIGHT22 FINAL STUDENT REPLICATE BRR-FAY WEIGHT23 FINAL STUDENT REPLICATE BRR-FAY WEIGHT24 FINAL STUDENT REPLICATE BRR-FAY WEIGHT25 FINAL STUDENT REPLICATE BRR-FAY WEIGHT26 FINAL STUDENT REPLICATE BRR-FAY WEIGHT27 FINAL STUDENT REPLICATE BRR-FAY WEIGHT28 FINAL STUDENT REPLICATE BRR-FAY WEIGHT29 FINAL STUDENT REPLICATE BRR-FAY WEIGHT30 FINAL STUDENT REPLICATE BRR-FAY WEIGHT31 FINAL STUDENT REPLICATE BRR-FAY WEIGHT32 FINAL STUDENT REPLICATE BRR-FAY WEIGHT33 FINAL STUDENT REPLICATE BRR-FAY WEIGHT34 FINAL STUDENT REPLICATE BRR-FAY WEIGHT35 FINAL STUDENT REPLICATE BRR-FAY WEIGHT36 FINAL STUDENT REPLICATE BRR-FAY WEIGHT37 FINAL STUDENT REPLICATE BRR-FAY WEIGHT38 FINAL STUDENT REPLICATE BRR-FAY WEIGHT39 FINAL STUDENT REPLICATE BRR-FAY WEIGHT40 FINAL STUDENT REPLICATE BRR-FAY WEIGHT41 FINAL STUDENT REPLICATE BRR-FAY WEIGHT42 FINAL STUDENT REPLICATE BRR-FAY WEIGHT43 FINAL STUDENT REPLICATE BRR-FAY WEIGHT44 FINAL STUDENT REPLICATE BRR-FAY WEIGHT45 FINAL STUDENT REPLICATE BRR-FAY WEIGHT46 FINAL STUDENT REPLICATE BRR-FAY WEIGHT47 FINAL STUDENT REPLICATE BRR-FAY WEIGHT48 FINAL STUDENT REPLICATE BRR-FAY WEIGHT49 FINAL STUDENT REPLICATE BRR-FAY WEIGHT50 FINAL STUDENT REPLICATE BRR-FAY WEIGHT51 FINAL STUDENT REPLICATE BRR-FAY WEIGHT52 FINAL STUDENT REPLICATE BRR-FAY WEIGHT53 FINAL STUDENT REPLICATE BRR-FAY WEIGHT54 FINAL STUDENT REPLICATE BRR-FAY WEIGHT55 FINAL STUDENT REPLICATE BRR-FAY WEIGHT56 FINAL STUDENT REPLICATE BRR-FAY WEIGHT57 FINAL STUDENT REPLICATE BRR-FAY WEIGHT58 FINAL STUDENT REPLICATE BRR-FAY WEIGHT59 FINAL STUDENT REPLICATE BRR-FAY WEIGHT60 FINAL STUDENT REPLICATE BRR-FAY WEIGHT61 FINAL STUDENT REPLICATE BRR-FAY WEIGHT62 FINAL STUDENT REPLICATE BRR-FAY WEIGHT63 FINAL STUDENT REPLICATE BRR-FAY WEIGHT64 FINAL STUDENT REPLICATE BRR-FAY WEIGHT65 FINAL STUDENT REPLICATE BRR-FAY WEIGHT66 FINAL STUDENT REPLICATE BRR-FAY WEIGHT67 FINAL STUDENT REPLICATE BRR-FAY WEIGHT68 FINAL STUDENT REPLICATE BRR-FAY WEIGHT69 FINAL STUDENT REPLICATE BRR-FAY WEIGHT70 FINAL STUDENT REPLICATE BRR-FAY WEIGHT71 FINAL STUDENT REPLICATE BRR-FAY WEIGHT72 FINAL STUDENT REPLICATE BRR-FAY WEIGHT73 FINAL STUDENT REPLICATE BRR-FAY WEIGHT74 FINAL STUDENT REPLICATE BRR-FAY WEIGHT75 FINAL STUDENT REPLICATE BRR-FAY WEIGHT76 FINAL STUDENT REPLICATE BRR-FAY WEIGHT77 FINAL STUDENT REPLICATE BRR-FAY WEIGHT78 FINAL STUDENT REPLICATE BRR-FAY WEIGHT79 FINAL STUDENT REPLICATE BRR-FAY WEIGHT80 RANDOMIZED FINAL VARIANCE STRATUM (1-80) RANDOMLY ASSIGNED VARIANCE UNIT Senate weight - sum of weight within the country is 1000 Date of the database creation
    324126 Mexico 4840000 MEX OECD Mexico 370 8424 10 7.0 3 1996 Male Yes, for more than one year 6.0 No, never No, never No, never None None 1.0 No No No No No Yes <ISCED level 1> NaN NaN NaN NaN NaN <ISCED level 1> NaN NaN NaN NaN Working part-time <for pay> Country of test Country of test Country of test NaN Language of the test Yes Yes Yes No No No Yes No No No No Yes No Yes 484002 484001 484002 One One NaN NaN None 11-25 books Agree Strongly agree Agree Agree Agree Agree Agree Strongly agree Agree Disagree Disagree Agree Agree Agree Confident Confident Not very confident Not very confident Confident Not very confident Confident Not very confident NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Strongly agree Agree Agree Disagree Agree Agree Likely Likely NaN Slightly likely Slightly likely Slightly likely Agree Agree Agree Agree Agree Agree Agree Agree Agree Courses after school Math Major in college Math Study harder Math Maximum classes Math Pursuing a career Math Sometimes Often Sometimes Sometimes Sometimes Never or rarely Never or rarely Never or rarely Most important new ways Relating to other subjects everyday life 4 or more but less than 6 hours a week 4 or more but less than 6 hours a week I do not attend <out-of-school time lessons> i... Less than 2 hours a week 3.0 NaN 0.0 0.0 1.0 2.0 Sometimes Rarely Never Rarely Frequently Sometimes Frequently Never Frequently Heard of it a few times Heard of it a few times Heard of it a few times Heard of it a few times Never heard of it Never heard of it Never heard of it Heard of it once or twice Heard of it once or twice Never heard of it Heard of it a few times Heard of it once or twice Never heard of it Heard of it a few times Heard of it once or twice Heard of it a few times 60.0 60.0 NaN 4.0 6.0 2.0 30.0 50.0 Sometimes Sometimes Sometimes Sometimes Frequently Sometimes Frequently Sometimes NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Somewhat like me Not much like me Somewhat like me Somewhat like me Somewhat like me Not much like me Somewhat like me Somewhat like me Not much like me Somewhat like me definitely do this probably do this definitely do this definitely do this 1.0 2.0 1.0 2.0 1.0 2.0 2.0 1.0 No No No Yes, but I donÂ’t use it Yes, but I donÂ’t use it Yes, and I use it Yes, and I use it Yes, and I use it Yes, but I donÂ’t use it Yes, and I use it Yes, but I donÂ’t use it No No No No Yes, and I use it Yes, and I use it Yes, and I use it 7-9 years old 13 years old or older 2 7 6 Once or twice a month Once or twice a month Once or twice a month Once or twice a month Never or hardly ever Once or twice a month Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Yes, but only the teacher demonstrated this Yes, but only the teacher demonstrated this Yes, but only the teacher demonstrated this Yes, but only the teacher demonstrated this Yes, but only the teacher demonstrated this Yes, but only the teacher demonstrated this Yes, but only the teacher demonstrated this Agree Agree Agree Agree Agree Agree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN No calculator 10 10 0 StQ Form A booklet 23 Easier set of booklets 16.00 0.0 Mexico: Technical Baccalaureate or Technical f... NaN NaN NaN NaN 12.34 NaN NaN Mexico Mexico Mexico NaN NaN -0.48 NaN -1.5967 -2.82 -0.6251 0.7955 -0.2626 -0.9054 -0.82 3.0 ISCED 1 -1.80 NaN ISCED 1 12.34 -2.71 -2.4442 NaN 1.0091 -0.5096 -0.6891 -2.47 -0.0836 Native NaN NaN NaN 0.80 0.91 A ISCED level 3 Vocational NaN Spanish NaN 240.0 0.6426 -0.77 1.4565 0.2882 ISCED 1 360.0 NaN Housewife Fishery and aquaculture labourers -0.9492 6.0 6.0 -0.1475 Did not repeat a <grade> NaN NaN NaN -0.0455 NaN NaN NaN NaN Spanish 114.0 1.0032 -1.6104 -2.75 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 466.4356 443.0675 476.5618 434.4991 469.5513 497.5931 414.2468 495.2563 414.2468 450.8568 452.4147 392.4365 430.6045 419.6993 405.6785 535.7610 498.3720 520.9612 504.6035 529.5295 450.8568 453.1936 445.4043 427.4887 462.5409 386.2050 493.6984 427.4887 448.5200 448.5200 443.8464 534.9821 500.7088 484.3511 503.0456 392.4365 500.7088 394.7733 450.0779 431.3834 406.4075 404.8036 408.0114 428.0600 313.3819 424.9720 427.7695 454.8116 427.7695 470.6639 45.3585 67.5590 69.9091 67.1650 70.1362 67.2642 67.9730 68.4704 67.8850 70.3181 69.9456 67.5336 70.0687 70.0249 67.8676 68.1256 70.8543 69.3333 70.2489 69.2551 67.6973 22.0079 22.8596 22.1369 22.7873 22.1066 21.8742 21.7175 21.9027 22.7283 22.8493 22.0167 22.8091 22.8241 21.9066 21.8247 22.5586 23.0535 22.7504 23.0794 21.9634 67.5590 69.9091 67.1650 70.1362 67.2642 67.9730 68.4704 67.8850 70.3181 69.9456 67.5336 70.0687 70.0249 67.8676 68.1256 70.8543 69.3333 70.2489 69.2551 67.6973 22.0079 22.8596 22.1369 22.7873 22.1066 21.8742 21.7175 21.9027 22.7283 22.8493 22.0167 22.8091 22.8241 21.9066 21.8247 22.5586 23.0535 22.7504 23.0794 21.9634 9 1 0.0342 22NOV13
    468383 Turkey 7920000 TUR OECD Turkey 106 2994 10 3.0 4 1996 Male No 6.0 No, never NaN No, never One or two times None 2.0 Yes Yes Yes Yes NaN NaN She did not complete <ISCED level 1> No No No NaN Other (e.g. home duties, retired) <ISCED level 1> No No No NaN Other (e.g. home duties, retired) Country of test Country of test Country of test NaN Other language No No Yes No No No No Yes No Yes No Yes No No 792002 792002 792002 One One None None One 11-25 books NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Agree Disagree Agree Disagree Agree Disagree Disagree Agree Disagree Agree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Most important Improve understanding learning goals everyday life I do not attend <out-of-school time lessons> i... I do not attend <out-of-school time lessons> i... I do not attend <out-of-school time lessons> i... I do not attend <out-of-school time lessons> i... 3.0 1.0 0.0 0.0 1.0 0.0 Never Rarely Rarely Rarely Frequently Rarely Frequently Sometimes Frequently Heard of it often Know it well, understand the concept Heard of it often Heard of it once or twice Heard of it often Know it well, understand the concept Heard of it a few times Heard of it often Heard of it often Never heard of it Heard of it often Heard of it once or twice Heard of it once or twice Know it well, understand the concept Know it well, understand the concept Know it well, understand the concept 40.0 40.0 40.0 5.0 4.0 4.0 40.0 15.0 Sometimes Sometimes Frequently Frequently Frequently Frequently Sometimes Sometimes Most Lessons Every Lesson Every Lesson Every Lesson Every Lesson Most Lessons Most Lessons Every Lesson Some Lessons Never or Hardly Ever Some Lessons Some Lessons Some Lessons Never or Hardly Ever Some Lessons Most Lessons Every Lesson Some Lessons Sometimes Often Often Often Sometimes Always or almost always Often Always or almost always Often Never or Hardly Ever Never or Hardly Ever Some Lessons Some Lessons Some Lessons Agree Agree Disagree Agree Agree Agree Agree Agree Strongly agree Strongly disagree Strongly agree Agree Strongly agree Disagree Agree Agree Agree Agree Agree Disagree Strongly agree Agree Agree Agree Strongly disagree Agree Strongly agree Strongly agree Agree Agree Disagree Agree Agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Strongly disagree Disagree Strongly agree Strongly disagree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN No No No No No Yes, and I use it No No No No No No No No No No No No Never NaN 99 99 99 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 10 10 0 StQ Form C booklet 6 Standard set of booklets 16.00 0.0 Turkey: Anatolian high school 0.79 -1.22 0.5206 0.32 11.56 NaN 0.7640 Turkey Turkey Turkey 0.3891 NaN -0.48 0.49 NaN -3.16 -0.6251 0.7955 NaN 0.7266 0.85 2.0 ISCED 1 -1.80 NaN ISCED 1 11.56 -3.23 NaN NaN NaN NaN -2.7874 -3.16 -2.8038 Native NaN NaN NaN NaN NaN A ISCED level 3 General NaN Another language (TUR) NaN 200.0 NaN NaN NaN NaN None 160.0 -0.2395 Housewife Field crop and vegetable growers NaN 5.0 5.0 NaN Did not repeat a <grade> -0.29 160.0 -0.02 NaN -0.2859 0.7183 -0.0798 0.97 Turkish NaN NaN NaN -3.73 -0.4110 0.0309 0.0501 0.2299 0.2717 NaN NaN NaN -0.1687 0.0358 -0.1113 NaN 433.2528 455.0631 435.5897 410.6637 435.5897 451.1684 417.6741 419.2320 409.8847 414.5583 436.3686 392.7481 429.3582 416.1162 419.2320 522.8307 437.9265 469.0840 482.3259 485.4417 456.6210 448.0527 436.3686 438.7054 435.5897 441.0422 465.1893 458.9578 423.1267 424.6845 399.7585 419.2320 400.5375 392.7481 392.7481 375.6115 401.3164 430.9160 399.7585 368.6010 445.8632 489.1682 448.2690 416.1912 455.4865 493.0436 496.7736 429.6345 424.0395 463.2040 180.7862 308.6213 52.9510 308.6213 308.6213 308.6213 308.6213 52.9510 52.9510 308.6213 52.9510 52.9510 308.6213 308.6213 52.9510 52.9510 52.9510 52.9510 308.6213 52.9510 308.6213 308.6213 52.9510 308.6213 308.6213 308.6213 308.6213 52.9510 52.9510 308.6213 52.9510 52.9510 308.6213 308.6213 52.9510 52.9510 52.9510 52.9510 308.6213 52.9510 308.6213 52.9510 308.6213 52.9510 52.9510 52.9510 52.9510 308.6213 308.6213 52.9510 308.6213 308.6213 52.9510 52.9510 308.6213 308.6213 308.6213 308.6213 52.9510 308.6213 52.9510 52.9510 308.6213 52.9510 52.9510 52.9510 52.9510 308.6213 308.6213 52.9510 308.6213 308.6213 52.9510 52.9510 308.6213 308.6213 308.6213 308.6213 52.9510 308.6213 52.9510 42 1 0.2086 22NOV13
    146667 Spain 7240200 ESP OECD Spain 38 1085 10 1.0 6 1996 Male Yes, for more than one year 6.0 No, never No, never NaN None None 2.0 Yes Yes No No No No <ISCED level 2> No No No Yes Other (e.g. home duties, retired) NaN No No No Yes Working full-time <for pay> Country of test Country of test Country of test NaN Language of the test Yes Yes Yes Yes No Yes Yes Yes Yes No No Yes Yes Yes 724001 724001 724001 Three or more Three or more One Two Two 101-200 books Strongly disagree Disagree Disagree Disagree Agree Disagree Disagree Agree Disagree Disagree Strongly disagree Strongly agree Agree Agree Not very confident Confident Confident Confident Very confident Not very confident Very confident Very confident Disagree Agree Disagree Agree Disagree Disagree Disagree Disagree Disagree Agree Agree Agree Disagree Disagree Agree Disagree Likely Slightly likely Slightly likely Likely Likely Slightly likely Agree Disagree Disagree Agree Disagree Agree Agree Agree Agree Courses after school Math Major in college Science Study harder Test Language Maximum classes Science Pursuing a career Science Never or rarely Sometimes Sometimes Never or rarely Never or rarely Never or rarely Never or rarely Never or rarely NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Most Lessons Most Lessons Most Lessons Most Lessons Some Lessons Most Lessons Some Lessons Every Lesson Some Lessons Some Lessons Most Lessons Never or Hardly Ever Some Lessons Never or Hardly Ever Never or Hardly Ever Some Lessons Every Lesson Most Lessons Often Sometimes Sometimes Often Sometimes Sometimes Never or rarely Always or almost always Always or almost always Some Lessons Most Lessons Every Lesson Some Lessons Some Lessons Agree Disagree Strongly disagree Agree Strongly agree Strongly agree Agree Strongly disagree Strongly agree Disagree Strongly agree Strongly agree Strongly agree Agree Disagree Agree Strongly agree Strongly agree Strongly agree Strongly disagree Strongly agree Strongly agree Strongly disagree Strongly agree Strongly disagree Strongly agree Strongly agree Strongly agree Disagree Disagree Agree Agree Strongly agree Strongly agree Agree Agree Agree Agree Disagree Disagree Agree Disagree Not much like me Not at all like me Very much like me Mostly like me Mostly like me Mostly like me Mostly like me Mostly like me Mostly like me Not much like me definitely not do this probably do this probably not do this probably do this 2.0 2.0 3.0 2.0 3.0 3.0 2.0 2.0 Yes, and I use it Yes, and I use it No Yes, and I use it Yes, and I use it Yes, and I use it Yes, and I use it Yes, and I use it Yes, and I use it Yes, and I use it No Yes, and I use it Yes, but I donÂ’t use it Yes, but I donÂ’t use it Yes, and I use it Yes, but I donÂ’t use it Yes, and I use it No 7-9 years old 7-9 years old 1 4 5 Once or twice a week Once or twice a week Once or twice a month Every day Every day Every day Once or twice a month Once or twice a month Every day Almost every day Once or twice a month Once or twice a month Once or twice a month Once or twice a month Once or twice a month Once or twice a month Once or twice a month Never or hardly ever Never or hardly ever Once or twice a month Never or hardly ever Once or twice a month Once or twice a month Never or hardly ever Never or hardly ever Never or hardly ever No Yes, but only the teacher demonstrated this No Yes, students did this No Yes, students did this No Agree Agree Agree Agree Agree Strongly agree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN A Scientific calculator 3 8 5 StQ Form B booklet 11 Standard set of booklets 15.92 0.0 Spain: Compulsory Secondary Education -0.20 -0.24 0.0873 2.63 25.95 27.91 0.7640 Aragon (ESP) Aragon (ESP) Aragon (ESP) -0.1777 NaN 1.27 -0.71 0.5490 -0.55 NaN NaN -0.0760 NaN NaN 2.0 ISCED 3A, ISCED 4 -1.29 NaN ISCED 3A, ISCED 4 27.91 0.40 0.0526 NaN 1.3219 -0.5096 0.7157 -1.13 0.8574 Native NaN NaN NaN -0.67 -0.66 A ISCED level 2 General NaN Spanish NaN NaN -0.4567 -0.02 -0.7332 -0.2356 ISCED 3A, ISCED 4 NaN 0.6709 Elementary workers not elsewhere classified Heavy truck and lorry drivers 0.0521 NaN 12.0 1.3116 Did not repeat a <grade> -0.29 NaN 0.81 -0.3852 -0.2859 0.4855 -0.0798 -0.28 Spanish 39.0 0.7306 -0.1388 0.45 -2.7779 -1.8911 -0.9890 -0.9781 -1.8184 -2.2172 -1.6958 -2.7430 -1.5787 -2.0134 -1.3056 -2.1828 544.4072 509.3550 554.5334 515.5865 540.5125 555.3124 538.1757 524.1549 552.1966 571.6700 549.8598 539.7336 521.0391 553.7545 558.4281 496.8920 522.5970 498.4499 478.1975 501.5657 603.6065 559.9860 560.7649 592.7014 592.7014 553.7545 533.5021 529.6074 538.9547 531.9442 539.7336 554.5334 525.7127 547.5230 549.8598 554.5334 559.2070 556.8702 568.5543 565.4385 530.7892 560.4612 534.7989 475.4550 547.6300 589.0899 595.6173 606.8072 543.3980 603.0772 7.1233 10.6850 10.6850 3.5617 10.6850 10.6850 10.6850 10.6850 3.5617 3.5617 10.6850 3.5617 3.5617 10.6850 10.6850 3.5617 3.5617 3.5617 3.5617 10.6850 3.5617 3.5617 3.5617 10.6850 3.5617 3.5617 3.5617 3.5617 10.6850 10.6850 3.5617 10.6850 10.6850 3.5617 3.5617 10.6850 10.6850 10.6850 10.6850 3.5617 10.6850 10.6850 10.6850 3.5617 10.6850 10.6850 10.6850 10.6850 3.5617 3.5617 10.6850 3.5617 3.5617 10.6850 10.6850 3.5617 3.5617 3.5617 3.5617 10.6850 3.5617 3.5617 3.5617 10.6850 3.5617 3.5617 3.5617 3.5617 10.6850 10.6850 3.5617 10.6850 10.6850 3.5617 3.5617 10.6850 10.6850 10.6850 10.6850 3.5617 10.6850 29 1 0.0191 22NOV13
    127931 Czech Republic 2030000 CZE OECD Czech Republic 11 158 9 1.0 7 1996 Male Yes, for one year or less 7.0 No, never No, never NaN None None 1.0 Yes Yes Yes Yes No No <ISCED level 3B, 3C> No No No Yes Working full-time <for pay> <ISCED level 3B, 3C> No No No Yes Working full-time <for pay> Country of test Country of test Country of test NaN Language of the test Yes Yes Yes Yes No Yes No No No No Yes Yes Yes Yes 203001 203001 203001 Three or more Three or more Two Two One 0-10 books Strongly disagree Strongly agree Disagree Disagree Strongly disagree Agree Strongly agree Strongly disagree Disagree Disagree Disagree Strongly agree Strongly agree Strongly disagree Confident Very confident Not very confident Very confident Very confident Not very confident Very confident Very confident Strongly disagree Strongly disagree Strongly disagree Disagree Strongly disagree Agree Strongly disagree Disagree Disagree Strongly disagree Strongly agree Strongly agree Strongly disagree Strongly disagree Agree Strongly disagree Not at all likely Not at all likely Not at all likely Very Likely Likely Not at all likely Strongly disagree Strongly disagree Strongly agree Strongly agree Disagree Strongly disagree Strongly disagree Disagree Strongly agree Courses after school Test Language Major in college Science Study harder Test Language Maximum classes Science Pursuing a career Science Never or rarely Never or rarely Never or rarely Never or rarely Never or rarely Sometimes Always or almost always Never or rarely NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Every Lesson Some Lessons Most Lessons Some Lessons Never or Hardly Ever Every Lesson Every Lesson Never or Hardly Ever Every Lesson Some Lessons Every Lesson Most Lessons Some Lessons Every Lesson Most Lessons Every Lesson Every Lesson Every Lesson Always or almost always Always or almost always Always or almost always Never or rarely Often Sometimes Always or almost always Always or almost always Often Every Lesson Never or Hardly Ever Never or Hardly Ever Some Lessons Never or Hardly Ever Disagree Disagree Disagree Strongly agree Agree Disagree Strongly disagree Disagree Strongly agree Disagree Disagree Disagree Strongly agree Strongly disagree Strongly agree Strongly agree Strongly agree Strongly agree Strongly agree Disagree Agree Agree Strongly disagree Agree Strongly disagree Strongly disagree Strongly disagree Disagree Disagree Agree Strongly disagree Strongly disagree Strongly disagree Agree Agree Strongly disagree Agree Strongly agree Strongly disagree Strongly disagree Agree Disagree Not at all like me Not at all like me Somewhat like me Not at all like me Very much like me Very much like me Very much like me Not at all like me Not at all like me Not at all like me definitely not do this definitely do this definitely not do this definitely not do this 4.0 4.0 4.0 4.0 1.0 4.0 2.0 4.0 Yes, and I use it Yes, and I use it Yes, and I use it Yes, and I use it Yes, and I use it No Yes, and I use it Yes, and I use it Yes, and I use it Yes, and I use it Yes, and I use it Yes, but I donÂ’t use it No No No Yes, but I donÂ’t use it No No 7-9 years old 7-9 years old 1 7 7 Never or hardly ever Every day Every day Every day Every day Every day Every day Every day Every day Every day Almost every day Every day Almost every day Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever No No No No No No No Strongly agree Strongly disagree Strongly disagree Strongly disagree Strongly disagree Strongly disagree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN No calculator 7 10 3 StQ Form B booklet 10 Standard set of booklets 15.75 0.0 Czech Republic: Basic school -1.68 -1.70 -2.1084 -0.90 25.95 43.33 -0.0784 Czech Republic Czech Republic Czech Republic 0.6994 NaN -1.51 0.20 1.8774 -0.33 NaN NaN -1.0202 NaN NaN 2.0 ISCED 3A, ISCED 4 -0.69 NaN ISCED 3A, ISCED 4 43.33 -0.30 0.4115 NaN -2.1575 -1.7471 1.4497 -0.40 -1.4611 Native NaN NaN NaN -0.67 -0.34 A ISCED level 2 General NaN Czech NaN NaN 0.2171 0.34 -1.5329 -0.8805 ISCED 3A, ISCED 4 NaN -0.9508 General office clerks Heavy truck and lorry drivers -0.9492 NaN 13.0 0.4795 Did not repeat a <grade> -0.06 NaN 2.16 -0.3852 1.0416 1.3823 1.0768 -0.66 Czech 137.0 -0.7749 -1.6104 0.44 -0.7454 -0.8775 -0.2191 -0.0983 0.4532 -0.1562 -0.0960 -0.2558 -0.3007 0.0358 1.0099 -0.1343 446.4948 428.5792 447.2737 456.6210 430.9160 419.2320 425.4635 465.9682 458.9578 447.2737 430.1371 418.4530 462.8525 466.7472 444.9369 411.4426 385.7377 442.6001 441.0422 405.9900 392.7481 380.2851 448.0527 431.6950 409.8847 436.3686 453.5052 455.0631 437.1475 446.4948 460.5157 469.0840 482.3259 448.0527 467.5261 462.0735 455.0631 460.5157 446.4948 451.9473 511.6227 462.7041 427.4185 444.2593 408.1718 517.1018 456.4901 507.7769 489.1272 423.8530 25.4929 12.7464 38.2393 38.2393 38.2393 38.2393 12.7464 38.2393 12.7464 38.2393 12.7464 12.7464 12.7464 12.7464 38.2393 38.2393 12.7464 38.2393 38.2393 12.7464 12.7464 12.7464 38.2393 38.2393 38.2393 38.2393 12.7464 38.2393 12.7464 38.2393 12.7464 12.7464 12.7464 12.7464 38.2393 38.2393 12.7464 38.2393 38.2393 12.7464 12.7464 12.7464 38.2393 38.2393 38.2393 38.2393 12.7464 38.2393 12.7464 38.2393 12.7464 12.7464 12.7464 12.7464 38.2393 38.2393 12.7464 38.2393 38.2393 12.7464 12.7464 12.7464 38.2393 38.2393 38.2393 38.2393 12.7464 38.2393 12.7464 38.2393 12.7464 12.7464 12.7464 12.7464 38.2393 38.2393 12.7464 38.2393 38.2393 12.7464 12.7464 43 2 0.3099 22NOV13
    166716 Spain 7241600 ESP OECD Spain 757 21134 9 1.0 7 1996 Male No NaN Yes, once No, never NaN One or two times None 1.0 Yes No Yes Yes No No <ISCED level 3A> NaN NaN NaN Yes Working full-time <for pay> <ISCED level 3A> NaN NaN Yes NaN Working full-time <for pay> Other country Other country Other country 10.0 Language of the test Yes NaN Yes Yes No Yes NaN Yes Yes Yes NaN Yes NaN Yes 724001 724001 724002 NaN Two Two One One 26-100 books Disagree Agree Disagree Disagree Agree Disagree Agree Agree Disagree Agree Disagree Strongly agree Strongly agree Agree Very confident Confident Not very confident Confident Very confident Not very confident Very confident Not very confident NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Agree Agree Agree Agree Agree Disagree Slightly likely Slightly likely Likely Likely Likely Likely Agree Disagree Disagree Disagree Agree Disagree Agree Agree Disagree Courses after school Math Major in college Math Study harder Test Language Maximum classes Science Pursuing a career Math Never or rarely Never or rarely Never or rarely Sometimes Never or rarely NaN Sometimes Never or rarely Most important Improve understanding in my sleep everyday life I do not attend <out-of-school time lessons> i... I do not attend <out-of-school time lessons> i... I do not attend <out-of-school time lessons> i... I do not attend <out-of-school time lessons> i... 2.0 0.0 0.0 0.0 0.0 0.0 Sometimes Sometimes Sometimes Never Frequently Rarely Frequently Rarely Frequently Heard of it once or twice Heard of it often Heard of it once or twice Heard of it a few times Know it well, understand the concept Never heard of it Heard of it often Heard of it often Heard of it once or twice Heard of it often Heard of it often Heard of it often Never heard of it Heard of it a few times Heard of it often Heard of it a few times 60.0 60.0 60.0 4.0 3.0 3.0 NaN 20.0 Frequently Frequently Frequently Frequently Frequently Sometimes Sometimes Sometimes NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN Somewhat like me Somewhat like me Somewhat like me Somewhat like me Mostly like me Somewhat like me Mostly like me Mostly like me Somewhat like me Somewhat like me definitely not do this probably do this definitely not do this probably do this 3.0 2.0 3.0 2.0 3.0 3.0 3.0 3.0 Yes, and I use it Yes, but I donÂ’t use it Yes, but I donÂ’t use it Yes, and I use it Yes, and I use it Yes, but I donÂ’t use it No Yes, but I donÂ’t use it Yes, but I donÂ’t use it Yes, but I donÂ’t use it Yes, but I donÂ’t use it Yes, but I donÂ’t use it Yes, but I donÂ’t use it No Yes, but I donÂ’t use it Yes, but I donÂ’t use it No No 10-12 years old 10-12 years old 1 4 6 Almost every day Almost every day Once or twice a month Once or twice a week Almost every day Almost every day Once or twice a week Once or twice a month Never or hardly ever Never or hardly ever Once or twice a month Never or hardly ever Never or hardly ever Never or hardly ever Never or hardly ever Once or twice a month Never or hardly ever Never or hardly ever Never or hardly ever Once or twice a month Never or hardly ever Never or hardly ever Never or hardly ever Once or twice a week Almost every day Almost every day No No No No No No No Agree Agree Agree Agree Disagree Agree NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN A Simple calculator 6 7 1 StQ Form A booklet 7 Standard set of booklets 15.83 -1.0 Spain: Compulsory Secondary Education NaN NaN NaN NaN 23.57 25.04 NaN Another country (ESP) Another country (ESP) Another country (ESP) NaN NaN 1.01 NaN -0.1819 -0.72 -0.2531 0.7955 0.1524 -0.2608 -1.52 1.0 ISCED 5B -0.43 NaN ISCED 5B 25.04 -0.27 -0.9986 NaN 0.7005 -0.5096 -0.5015 -0.40 -0.7538 First-Generation NaN NaN NaN 0.05 -0.34 A ISCED level 2 General NaN Spanish NaN 240.0 -0.3551 -0.18 0.1775 -0.5641 ISCED 3A, ISCED 4 180.0 NaN Waiters Bakers, pastry-cooks and confectionery makers -0.1465 2.0 13.0 -0.1475 Repeated a <grade> NaN 180.0 NaN 0.6602 NaN NaN NaN NaN Spanish 56.0 -0.7749 0.6698 -0.54 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 479.2881 476.9512 499.5404 487.8564 486.2985 527.5822 540.0452 467.6040 519.7928 527.5822 517.4560 506.5509 475.3934 520.5717 556.4029 541.6031 538.4873 493.3089 562.6344 550.9503 520.5717 485.5196 487.8564 509.6666 522.9086 480.0670 447.3516 498.7615 496.4247 490.1932 494.0879 501.0983 530.6979 555.6239 531.4769 506.5509 477.7302 549.3924 550.1714 506.5509 538.8888 516.4344 496.3857 497.9896 502.8013 493.6964 510.4812 475.9791 503.9537 520.7385 3.0499 4.5748 4.5748 4.5748 4.5748 1.5249 1.5249 4.5748 1.5249 1.5249 4.5748 4.5748 1.5249 1.5249 1.5249 1.5249 4.5748 1.5249 4.5748 1.5249 4.5748 4.5748 4.5748 4.5748 4.5748 1.5249 1.5249 4.5748 1.5249 1.5249 4.5748 4.5748 1.5249 1.5249 1.5249 1.5249 4.5748 1.5249 4.5748 1.5249 4.5748 4.5748 4.5748 4.5748 4.5748 1.5249 1.5249 4.5748 1.5249 1.5249 4.5748 4.5748 1.5249 1.5249 1.5249 1.5249 4.5748 1.5249 4.5748 1.5249 4.5748 4.5748 4.5748 4.5748 4.5748 1.5249 1.5249 4.5748 1.5249 1.5249 4.5748 4.5748 1.5249 1.5249 1.5249 1.5249 4.5748 1.5249 4.5748 1.5249 4.5748 68 1 0.0082 22NOV13

    Wrangling

    In [36]:
    # Spliting the raw data into different smaller dataframes for easier analysis
    # first the main data frame containing the student and schoold information along with the 3 desired variables to be analyysed 
    
    main_df = raw_data.iloc[:, np.r_[6:13, 0,2,3,178,500,540,545, 460,466, 476, 484,
                                    60:63, 65, 74:80, 132:136]].copy()
    
    In [37]:
    # print the summary for the main dataframe
    main_df.info()
    
    <class 'pandas.core.frame.DataFrame'>
    RangeIndex: 485490 entries, 0 to 485489
    Data columns (total 32 columns):
     #   Column                                                                                Non-Null Count   Dtype  
    ---  ------                                                                                --------------   -----  
     0   Student ID                                                                            485490 non-null  int64  
     1   International Grade                                                                   485490 non-null  int64  
     2   National Study Programme                                                              485438 non-null  float64
     3   Birth - Month                                                                         485490 non-null  int64  
     4   Birth -Year                                                                           485490 non-null  int64  
     5   Gender                                                                                485490 non-null  object 
     6   Attend <ISCED 0>                                                                      476166 non-null  object 
     7   Country code 3-character                                                              485490 non-null  object 
     8   Stratum ID 7-character (cnt + region ID + original stratum ID)                        485490 non-null  object 
     9   OECD country                                                                          485490 non-null  object 
     10  Class Size - No of Students in <Test Language> Class                                  294163 non-null  float64
     11  Plausible value 1 in mathematics                                                      485490 non-null  float64
     12  Plausible value 1 in reading                                                          485490 non-null  float64
     13  Plausible value 1 in science                                                          485490 non-null  float64
     14  Learning time (minutes per week)  - <test language>                                   282866 non-null  float64
     15  Learning time (minutes per week)- <Mathematics>                                       283303 non-null  float64
     16  Learning time (minutes per week) - <Science>                                          270914 non-null  float64
     17  Time of computer use (mins)                                                           297074 non-null  float64
     18  How many - cellular phones                                                            477079 non-null  object 
     19  How many - televisions                                                                476548 non-null  object 
     20  How many - computers                                                                  473459 non-null  object 
     21  How many books at home                                                                473765 non-null  object 
     22  Subjective Norms -Friends Do Well in Mathematics                                      315860 non-null  object 
     23  Subjective Norms -Friends Work Hard on Mathematics                                    315315 non-null  object 
     24  Subjective Norms - Friends Enjoy Mathematics Tests                                    314873 non-null  object 
     25  Subjective Norms - Parents Believe Studying Mathematics Is Important                  315160 non-null  object 
     26  Subjective Norms - Parents Believe Mathematics Is Important for Career                314843 non-null  object 
     27  Subjective Norms - Parents Like Mathematics                                           313389 non-null  object 
     28  Learning Strategies- Important Parts vs. Existing Knowledge vs. Learn by Heart        309947 non-null  object 
     29  Learning Strategies- Improve Understanding vs. New Ways vs. Memory                    309880 non-null  object 
     30  Learning Strategies - Other Subjects vs. Learning Goals vs. Rehearse Problems         309272 non-null  object 
     31  Learning Strategies - Repeat Examples vs. Everyday Applications vs. More Information  308931 non-null  object 
    dtypes: float64(9), int64(4), object(19)
    memory usage: 118.5+ MB
    
    In [38]:
    # rename columns for easier analysis
    main_df.rename(columns={"Country code 3-character": "country"  ,"Stratum ID 7-character (cnt + region ID + original stratum ID)": "reg_id"\
                            , "Class Size - No of Students in <Test Language> Class": "class_size"\
                           ,"Plausible value 1 in mathematics": "pv1_math"\
                           ,"Plausible value 1 in reading": 'pv1_reading'\
                           ,"Plausible value 1 in science": 'pv1_science'\
                  ,"Learning time (minutes per week)  - <test language>": "learn_time_lang"\
                            ,"Learning time (minutes per week)- <Mathematics>":"learn_time_math"
                  ,"Learning time (minutes per week) - <Science>": "learn_time_science"}, inplace=True);
    
    In [39]:
    # extract column name list
    col_names = main_df.columns.tolist()
    
    #print columns with enumerate to have an index for each variable
    print(list(enumerate(col_names)))
    
    [(0, 'Student ID'), (1, 'International Grade'), (2, 'National Study Programme'), (3, 'Birth - Month'), (4, 'Birth -Year'), (5, 'Gender'), (6, 'Attend <ISCED 0>'), (7, 'country'), (8, 'reg_id'), (9, 'OECD country'), (10, 'class_size'), (11, 'pv1_math'), (12, 'pv1_reading'), (13, 'pv1_science'), (14, 'learn_time_lang'), (15, 'learn_time_math'), (16, 'learn_time_science'), (17, 'Time of computer use (mins)'), (18, 'How many - cellular phones'), (19, 'How many - televisions'), (20, 'How many - computers'), (21, 'How many books at home'), (22, 'Subjective Norms -Friends Do Well in Mathematics'), (23, 'Subjective Norms -Friends Work Hard on Mathematics'), (24, 'Subjective Norms - Friends Enjoy Mathematics Tests'), (25, 'Subjective Norms - Parents Believe Studying Mathematics Is Important'), (26, 'Subjective Norms - Parents Believe Mathematics Is Important for Career'), (27, 'Subjective Norms - Parents Like Mathematics'), (28, 'Learning Strategies- Important Parts vs. Existing Knowledge vs. Learn by Heart'), (29, 'Learning Strategies- Improve Understanding vs. New Ways vs. Memory'), (30, 'Learning Strategies - Other Subjects vs. Learning Goals vs. Rehearse Problems'), (31, 'Learning Strategies - Repeat Examples vs. Everyday Applications vs. More Information')]
    
    In [40]:
    # print summart statistics for main dataframe
    main_df.head(2)
    
    Out[40]:
    x Student ID International Grade National Study Programme Birth - Month Birth -Year Gender Attend <ISCED 0> country reg_id OECD country class_size pv1_math pv1_reading pv1_science learn_time_lang learn_time_math learn_time_science Time of computer use (mins) How many - cellular phones How many - televisions How many - computers How many books at home Subjective Norms -Friends Do Well in Mathematics Subjective Norms -Friends Work Hard on Mathematics Subjective Norms - Friends Enjoy Mathematics Tests Subjective Norms - Parents Believe Studying Mathematics Is Important Subjective Norms - Parents Believe Mathematics Is Important for Career Subjective Norms - Parents Like Mathematics Learning Strategies- Important Parts vs. Existing Knowledge vs. Learn by Heart Learning Strategies- Improve Understanding vs. New Ways vs. Memory Learning Strategies - Other Subjects vs. Learning Goals vs. Rehearse Problems Learning Strategies - Repeat Examples vs. Everyday Applications vs. More Information
    0 1 10 1.0 2 1996 Female No Albania ALB Non-OECD NaN 406.8469 249.5762 341.7009 NaN NaN NaN NaN Two One None 0-10 books Disagree Agree Disagree Agree Agree Agree NaN NaN NaN NaN
    1 2 10 1.0 2 1996 Female Yes, for more than one year Albania ALB Non-OECD 30.0 486.1427 406.2936 548.9929 315.0 270.0 90.0 NaN Three or more Three or more Three or more 201-500 books Strongly agree Strongly agree Disagree Agree Disagree Agree relating to known Improve understanding in my sleep Repeat examples
    In [41]:
    # print summart statistics for main dataframe
    main_df.describe()
    
    Out[41]:
    x Student ID International Grade National Study Programme Birth - Month Birth -Year class_size pv1_math pv1_reading pv1_science learn_time_lang learn_time_math learn_time_science Time of computer use (mins)
    count 485490.000000 485490.000000 485438.000000 485490.000000 485490.000000 294163.000000 485490.000000 485490.000000 485490.000000 282866.000000 283303.000000 270914.000000 297074.000000
    mean 6134.066201 9.813323 2.579260 6.558512 1996.070061 26.017759 469.621653 472.004640 475.769824 219.276636 226.007056 211.122460 50.895996
    std 6733.144944 3.734726 2.694013 3.705244 0.255250 9.223134 103.265391 102.505523 101.464426 97.997730 97.448421 131.368322 40.987895
    min 1.000000 7.000000 1.000000 1.000000 1996.000000 0.000000 19.792800 0.083400 2.648300 0.000000 0.000000 0.000000 0.000000
    25% 1811.000000 9.000000 1.000000 4.000000 1996.000000 20.000000 395.318600 403.600700 404.457300 165.000000 180.000000 120.000000 19.000000
    50% 3740.000000 10.000000 1.000000 7.000000 1996.000000 25.000000 466.201900 475.455000 475.699400 200.000000 220.000000 180.000000 39.000000
    75% 7456.000000 10.000000 3.000000 9.000000 1996.000000 30.000000 541.057800 544.502500 547.780700 250.000000 250.000000 270.000000 71.000000
    max 33806.000000 96.000000 25.000000 99.000000 1997.000000 200.000000 962.229300 904.802600 903.338300 2400.000000 3000.000000 2975.000000 206.000000

    seems no outliers or annmolies are present, which will be verified visually in the exploration section

    In [42]:
    # checking for invalid country name and codes
    
    list_name = [i.name for i in list(pycountry.countries)]
    list_alpha_3 = [i.alpha_3 for i in list(pycountry.countries)]    
    
    def country_flag(main_df):
        if (main_df['country'] in list_name):
            return pycountry.countries.get(name=main_df['country']).alpha_3
        else:
            return 'Invalid Code'
    
    main_df['country_code']=main_df.apply(country_flag, axis = 1)
    
    In [43]:
    # change the values of invalid codes
    code_dict = {"Czech Republic": "CZE", "Korea":"KOR", "Slovak Republic":"SVK", "United States of America":"USA", "Vietnam":"VNM"}
    
    main_df.loc[main_df.country == "Czech Republic", "country_code"] = "CZE"
    main_df.loc[main_df.country == "Korea", "country_code"] = "KOR"
    main_df.loc[main_df.country == "Slovak Republic", "country_code"] = "SVK"
    main_df.loc[main_df.country == "United States of America", "country_code"] = "USA"
    main_df.loc[main_df.country == "Vietnam", "country_code"] = "VNM"
    main_df.loc[main_df.country == "China", "country_code"] = "CHN"
    main_df.loc[main_df.country == "Russian Federation", "country_code"] = "RUS"
    main_df.loc[main_df.country == "Macao", "country_code"] = "MAC"
    main_df.loc[main_df.country == "Hong Kong", "country_code"] = "HKG"
    
    In [44]:
    # define a function to rename both country and its 3 charachter code
    def rename_country(df, series, name, reg_id, country_code):
        """
        Rename a value in a series
        
        Parameters:
        df: dataframe
        series : name of the variable
        name :desired name
        reg_id : Region name to be replaced
        country_code: country code to replace the region name
        
        Returns:
        dataframe with correct country name and region name replaced with desired country code
        """
        df[df["country"] ==series] =df.query("country ==@series").replace(series,name)
        df.query("country ==@name")[reg_id].replace(reg_id, country_code)
    
    In [45]:
    usa_States = ['Florida (USA)', 'Connecticut (USA)', 'Massachusetts (USA)']
    for states in usa_States:
        try:
            rename_country(main_df,states, 'United States of America', 'reg_id','USA');
        except:
            pass
    
    In [46]:
    # correct some of the invalid names and country codes
    rename_country(main_df,'Massachusetts (USA)', 'United States of America', 'reg_id','USA');
    
    In [47]:
    # correct some of the invalid names and country codes
    rename_country(main_df,'Hong Kong-China', 'Hong Kong', 'reg_id','HKG');
    
    In [48]:
    # correct some of the invalid names and country codes
    rename_country(main_df,'Macao-China', 'Macao', 'reg_id','MAC');
    
    In [49]:
    # correct some of the invalid names and country codes
    rename_country(main_df,'China-Shanghai', 'China', 'reg_id','CHN');
    
    In [50]:
    # correct some of the invalid names and country codes
    rename_country(main_df,'Chinese Taipei', 'China', 'reg_id','CHN')
    
    In [51]:
    # correct some of the invalid names and country codes
    rename_country(main_df,'Perm(Russian Federation)', 'Russian Federation', 'reg_id','RUS')
    
    In [52]:
    # create a function to order the ordinal categorical  featueres
    def reorder_ordinal(categories_order, dataframe,series):
        """
        Re-order Ordinal Variables
        categories_order : list of ordered variables
        dataframe: dataframe to be used
         series: the series whose order will be changed   
        """
        ordered_cat = pd.api.types.CategoricalDtype(ordered = True, categories = categories_order)
        dataframe[series] = dataframe[series].astype(ordered_cat)
    
    In [53]:
    # re-order subjective norms for better visualization
    norms_list=main_df.columns.tolist()[22:28]
    norm_order= ["nan",'Strongly disagree', 'Disagree', 'Agree', 'Strongly agree']
    
    In [54]:
    #use function to re-order all subjective norms featuers 
    for norm in norms_list:
        reorder_ordinal(norm_order, main_df, norm)
    
    In [55]:
    main_df.head(2)
    
    Out[55]:
    x Student ID International Grade National Study Programme Birth - Month Birth -Year Gender Attend <ISCED 0> country reg_id OECD country class_size pv1_math pv1_reading pv1_science learn_time_lang learn_time_math learn_time_science Time of computer use (mins) How many - cellular phones How many - televisions How many - computers How many books at home Subjective Norms -Friends Do Well in Mathematics Subjective Norms -Friends Work Hard on Mathematics Subjective Norms - Friends Enjoy Mathematics Tests Subjective Norms - Parents Believe Studying Mathematics Is Important Subjective Norms - Parents Believe Mathematics Is Important for Career Subjective Norms - Parents Like Mathematics Learning Strategies- Important Parts vs. Existing Knowledge vs. Learn by Heart Learning Strategies- Improve Understanding vs. New Ways vs. Memory Learning Strategies - Other Subjects vs. Learning Goals vs. Rehearse Problems Learning Strategies - Repeat Examples vs. Everyday Applications vs. More Information country_code
    0 1 10 1.0 2 1996 Female No Albania ALB Non-OECD NaN 406.8469 249.5762 341.7009 NaN NaN NaN NaN Two One None 0-10 books Disagree Agree Disagree Agree Agree Agree NaN NaN NaN NaN ALB
    1 2 10 1.0 2 1996 Female Yes, for more than one year Albania ALB Non-OECD 30.0 486.1427 406.2936 548.9929 315.0 270.0 90.0 NaN Three or more Three or more Three or more 201-500 books Strongly agree Strongly agree Disagree Agree Disagree Agree relating to known Improve understanding in my sleep Repeat examples ALB
    In [56]:
    # re-order how many ict devices are owned
    ict_list=['nan','None', 'One', 'Two', 'Three or more']
    ict_columns = main_df.columns.tolist()[18:21]
    for series in ict_columns:
        reorder_ordinal(ict_list, main_df, series)
    
    In [57]:
    # re-order how many books variable
    books_order= ["nan",'0-10 books ', '11-25 books' , '26-100 books ','101-200 books '
                  '201-500 books ', 'More than 500 books' ]
    reorder_ordinal(books_order, main_df,'How many books at home')
    
    In [58]:
    # change the values of invalid codes
    code_dict = {"Czech Republic": "CZE", "Korea":"KOR", "Slovak Republic":"SVK", "United States of America":"USA", "Vietnam":"VNM"}
    
    main_df.loc[main_df.country == "Czech Republic", "country_code"] = "CZE"
    main_df.loc[main_df.country == "Korea", "country_code"] = "KOR"
    main_df.loc[main_df.country == "Slovak Republic", "country_code"] = "SVK"
    main_df.loc[main_df.country == "United States of America", "country_code"] = "USA"
    main_df.loc[main_df.country == "Vietnam", "country_code"] = "VNM"
    main_df.loc[main_df.country == "China", "country_code"] = "CHN"
    main_df.loc[main_df.country == "Russian Federation", "country_code"] = "RUS"
    main_df.loc[main_df.country == "Macao", "country_code"] = "MAC"
    main_df.loc[main_df.country == "Hong Kong", "country_code"] = "HKG"
    
    In [59]:
    # score per country
    main_group_country = main_df.groupby(['country', 'country_code']).agg({'pv1_math': ['mean']\
                                                                      ,"pv1_reading": ['mean']\
                                                                       ,"pv1_science":['mean']\
                                                                          ,'class_size':'mean', 'learn_time_lang':"mean"\
                                                                          ,'learn_time_math':"mean"\
                                                                          ,'learn_time_science':"mean"\
                                                                          ,'Time of computer use (mins)':'mean'})
    
    In [60]:
    # to unstack the grouped  variables
    main_group_country.columns=main_group_country.columns.map('_'.join)
    
    In [61]:
    # to convert it as a dataframe for easier manipulation
    country_mean_score = main_group_country.reset_index()
    
    In [62]:
    # view the head of datafrane
    country_mean_score
    
    Out[62]:
    country country_code pv1_math_mean pv1_reading_mean pv1_science_mean class_size_mean learn_time_lang_mean learn_time_math_mean learn_time_science_mean Time of computer use (mins)_mean
    0 Albania ALB 395.296185 396.424292 399.068070 26.219784 175.873077 171.165197 149.358542 NaN
    1 Argentina ARG 395.402241 403.511227 410.369278 28.236729 266.146698 278.499787 222.214189 NaN
    2 Australia AUS 492.842855 500.845303 511.250599 22.109969 233.019440 235.801302 227.791550 66.966664
    3 Austria AUT 507.711753 490.918021 507.213082 20.804536 143.464957 155.840096 199.740586 48.257309
    4 Belgium BEL 519.868902 512.319008 510.475079 18.671750 217.177139 216.420305 190.755675 49.932816
    ... ... ... ... ... ... ... ... ... ... ...
    58 United Arab Emirates ARE 431.373626 437.239951 445.089129 23.881731 268.238778 296.731890 297.135956 NaN
    59 United Kingdom GBR 489.645545 498.292310 510.043154 24.037055 230.741038 230.146636 284.280249 NaN
    60 United States of America USA 485.632260 502.823313 501.420740 23.120396 257.804277 257.119482 256.431326 NaN
    61 Uruguay URY 410.957687 413.573968 418.086895 25.096517 139.161391 156.538776 154.197560 56.757315
    62 Vietnam VNM 510.589967 508.259166 528.157948 40.996757 193.068268 226.802486 237.755485 NaN

    63 rows × 10 columns

    In [63]:
    #Check  for country names are correct
    main_df.country.unique()
    
    Out[63]:
    array(['Albania', 'United Arab Emirates', 'Argentina', 'Australia',
           'Austria', 'Belgium', 'Bulgaria', 'Brazil', 'Canada',
           'Switzerland', 'Chile', 'Colombia', 'Costa Rica', 'Czech Republic',
           'Germany', 'Denmark', 'Spain', 'Estonia', 'Finland', 'France',
           'United Kingdom', 'Greece', 'Hong Kong', 'Croatia', 'Hungary',
           'Indonesia', 'Ireland', 'Iceland', 'Israel', 'Italy', 'Jordan',
           'Japan', 'Kazakhstan', 'Korea', 'Liechtenstein', 'Lithuania',
           'Luxembourg', 'Latvia', 'Macao', 'Mexico', 'Montenegro',
           'Malaysia', 'Netherlands', 'Norway', 'New Zealand', 'Peru',
           'Poland', 'Portugal', 'Qatar', 'China', 'Russian Federation',
           'United States of America', 'Romania', 'Singapore', 'Serbia',
           'Slovak Republic', 'Slovenia', 'Sweden', 'Thailand', 'Tunisia',
           'Turkey', 'Uruguay', 'Vietnam'], dtype=object)
    In [64]:
    # check if there are any Invalid Codes
    country_mean_score.query("country_code=='Invalid Code'")
    
    Out[64]:
    country country_code pv1_math_mean pv1_reading_mean pv1_science_mean class_size_mean learn_time_lang_mean learn_time_math_mean learn_time_science_mean Time of computer use (mins)_mean
    In [65]:
    # change the structure of the data frame for visualization needs
    melted_main_group_country = country_mean_score.melt(['country', 'country_code'], var_name='score', value_name='vals')
    
    In [66]:
    #visualy inspect the data frame
    melted_main_group_country
    
    Out[66]:
    country country_code score vals
    0 Albania ALB pv1_math_mean 395.296185
    1 Argentina ARG pv1_math_mean 395.402241
    2 Australia AUS pv1_math_mean 492.842855
    3 Austria AUT pv1_math_mean 507.711753
    4 Belgium BEL pv1_math_mean 519.868902
    ... ... ... ... ...
    499 United Arab Emirates ARE Time of computer use (mins)_mean NaN
    500 United Kingdom GBR Time of computer use (mins)_mean NaN
    501 United States of America USA Time of computer use (mins)_mean NaN
    502 Uruguay URY Time of computer use (mins)_mean 56.757315
    503 Vietnam VNM Time of computer use (mins)_mean NaN

    504 rows × 4 columns

    Seems no erronus observation is noticed, so we can head over to the univariate analysis

    exploratory visualization

    Univariate Exploration

    In [67]:
    # plotting the 3 Variables of interest
    # the math score distribution
    main_interest = ['pv1_math','pv1_reading' ,'pv1_science']
    plt.figure(figsize=(10,6))
    for series in main_interest:
        main_df[series].plot(kind="hist", alpha=0.3, bins=20);       
    plt.legend(bbox_to_anchor=(1.04,1), loc="upper left")
    
    plt.show()
    

    from the graph above it shows that the scores of interest are normally distributed, without many outliers, also the distribution mean is in the following ranges:

    Pv1 for math : 425
    Pv1 for reading is around 550
    Pv1 for science : 425

    Now we explore the other numerical features{ Time spent studying and time spent using the PC}

    In [68]:
    # plotting the some of the variables that might affect the pv scores
    main_factors = ['learn_time_lang','learn_time_math', 'learn_time_science',	'Time of computer use (mins)']
    
    fig = plt.figure()
    fig.set_size_inches([20,20])
    for c,num in zip(main_factors, np.arange(1,5)):
        ax = fig.add_subplot(5,5,num)
        main_df[c].plot(kind="hist", title=c, ax=ax)
    plt.tight_layout()
    plt.show()
    

    Discuss the distribution(s) of your variable(s) of interest. Were there any unusual points? Did you need to perform any transformations?

    The graphs seems all are right skewed dstributed, with math and science learning time seems very closely related, while for reading languages, students seem to spent more time compared to math and science.

    while time spent using the computer seems has more wider variations, could it have an effect on the scores ?

    Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

    Right skewed dstributedm without any noticable unusual distributions, for these particular features, I haven't introduced any changed or wrangling techniques.

    Explore the Gender Distribution
    In [69]:
    # inspecting the gender count
    plt.figure(figsize=(8,4))
    base_color = sns.color_palette()[0]
    sns.countplot(data = main_df, x = 'Gender', color = base_color)
    plt.xticks(rotation = 0);
    

    We notice that the Gender is almost equally distributed, so this will help in giving us a better understanding if there is a relationship between Gender and the 3 Scores without any bias in the upcoming bivariate visualization section.

    Explore the top 10 scoring countries

    In [70]:
    # top scoring countries
    mean_score = ['pv1_math_mean', 'pv1_science_mean', 'pv1_reading_mean']
    
    for mean_score in mean_score:
        sns.catplot(y='country', x='vals',\
                  data=melted_main_group_country.query('score== @mean_score') \
               , kind='bar'\
               ,order=melted_main_group_country.\
                    query('score== @mean_score').sort_values(by='vals', ascending=False).iloc[:10]['country'],
                   color="c")
    
    We notice that there are several countries which shows in the top 10 countries repeatedly in the 3 scores, interestingly Asian countries have a strong presence in all 3 scores, they must be doing something different, to have such consisetant high scores.

    We need to investigate Asian countries behaviour vs other countries

    Bivariate Exploration

    In this section, investigate relationships between pairs of variables in your data. Make sure the variables that you cover here have been introduced in some fashion in the previous section (univariate exploration).

    In [71]:
    # investigate  Score  & class size vs Scores.
    corr = country_mean_score.corr()
    
    plt.figure(figsize = [8, 5])
    sns.heatmap(corr, annot = True, fmt = '.3f',   cmap = 'vlag_r', center = 0);
    

    We Notice that the Class size have weak negative correlation, with the score, to what extent will it affect the scores? we will find out in scores by country later on.

    In [72]:
    main_score_by_gender = main_df[['Gender', 'pv1_math', "pv1_reading", "pv1_science"]]
    
    scores =['pv1_math', "pv1_reading", "pv1_science"]
    for score in scores:
             sns.catplot(x="Gender", y=score, kind="box", dodge=False, data=main_score_by_gender);
    
    from the above graph, we notice that the distribution of the scores are very close for the reading and math scores, with males always having greater range of values, but both females and males have around the same mean of 500, while for the reading scores we observe that on average female have higher scores than males, which we will investigate further.

    otherwise, no large differences between genders

    In [73]:
    learn_time_by_gender = main_df[['Gender', 'learn_time_math', "learn_time_lang", "learn_time_science",  
                                    'Time of computer use (mins)']]
    
    scores =['learn_time_math', "learn_time_lang", "learn_time_science",  'Time of computer use (mins)']
    for score in scores:
             sns.catplot(x="Gender", y=score, kind="violin", dodge=False, data=learn_time_by_gender);
    

    we Observe some variances between time spent studying math as distribution of males have wider range, vicer versa for females their distribution has wider range for time spent studying a language and science, with males having a slight more time spent on using the computer for an average between booth Genders.

    Multivariate Exploration

    Create plots of three or more variables to investigate your data even further. Make sure that your investigations are justified, and follow from your work in the previous sections.

    In [74]:
    # Does gender has effect on score
    plt.figure(figsize=(10,6))
    sns.pairplot(main_score_by_gender, hue='Gender');
    
    <Figure size 720x432 with 0 Axes>
    In [75]:
    # plot the distribution of score around the world
    
    def score_per_country(dataframe, locations, variable,country, bar_title, title_name):
        fig = go.Figure(data=go.Choropleth(
        locations = dataframe[locations],
        z = dataframe[variable],
        text = dataframe[country].astype(str),
        colorscale = 'Blues',
        autocolorscale=False,
        reversescale=False,
        marker_line_color='darkgray',
        marker_line_width=0.5,
        colorbar_tickprefix = '#',
        colorbar_title = variable,
        ))
    
        fig.update_layout(
        title_text= title_name+ ' 2012 score',
        geo=dict(
            showframe=False,
            showcoastlines=False,
            projection_type='equirectangular'
        ),
        annotations = [dict(
            x=0.55,
            y=0.1,
            xref='paper',
            yref='paper',
            text='Source: <a href="http://www.oecd.org/pisa/keyfindings/pisa-2012-results.html">\
               PISA 2012</a>',
            showarrow = False
        )]
        )
    
        fig.show()
    
    the reason the below visualization is in the multivariate section, is I intend to compare score vs it's relevant study time + class size
    In [76]:
    # choropleth visualization for easier grasp of scores.
    score_mean = ['pv1_math_mean',  "learn_time_math_mean", 'pv1_reading_mean', "learn_time_lang_mean"\
                  ,'pv1_science_mean'\
                                             ,"learn_time_science_mean","class_size_mean" ]
    for score in score_mean:
        score_per_country(country_mean_score, 'country_code', score,'country',score, score)
    

    Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

    We Observe that some countries might have longer average study times yet other countries have better scores, this is constantly occurring with China for example, also class size should have played some rule in China having lower scores compared to other countries yet that didn't happen

    Of the features you investigated, were there any unusual distributions? Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

    Some Countries didn't show up on the map, so we had to verify the coutry names and codes to reflect all countries that are in the survey are correctly mapped.

    Did you observe any interesting relationships between the other features (not the main feature(s) of interest)?

    Canada, USA and Russia among the top countries that students dedicate long study hours especially in science.

    We Will now investigate what drives the Chinese students to score more, compared to other students who spent more time and have better student to teacher ratio.

    Why China has better scores ?

    In [77]:
    # compare china vs other coutries having on average median scores
    country_mean_score.median()
    
    Out[77]:
    pv1_math_mean                       485.632260
    pv1_reading_mean                    485.592069
    pv1_science_mean                    491.698981
    class_size_mean                      24.066823
    learn_time_lang_mean                203.366254
    learn_time_math_mean                212.221565
    learn_time_science_mean             196.941037
    Time of computer use (mins)_mean     54.317095
    dtype: float64
    In [78]:
    country_median_math= country_mean_score[country_mean_score["pv1_math_mean"].between(480,486)
    ]
    
    In [79]:
    middle_score_countries= country_median_math.country.values.tolist()
    
    In [80]:
    middle_score_countries
    
    Out[80]:
    ['Hungary',
     'Portugal',
     'Russian Federation',
     'Slovak Republic',
     'Slovenia',
     'United States of America']
    In [81]:
    # create a  subset dataframe for  country= china, and honk kong from main_df
    china_df= main_df.query('country==["China", "Hong Kong","Macao"]')
    
    In [82]:
    # create new data frame for countries with middle mean score for math
    mid_score_countries =main_df.query('country==["Slovenia", "Portugal", "United States of America"]')
    
    In [83]:
    # function to organise and  visualize the two df
    
    def visualize(dataframe1, dataframe2, series):
        dff=dataframe1.groupby(series).agg({'pv1_math':['mean','count'],
                                           'pv1_reading':['mean'],
                                            'pv1_science':['mean'] }).reset_index()
        dff.columns=[series,'pv1_math_mean',
                     'count','pv1_reading_mean', 'pv1_science_mean']
        dff2=dataframe2.groupby(series).agg({'pv1_math':['mean','count'],
                                           'pv1_reading':['mean'],
                                            'pv1_science':['mean'] }).reset_index()
        dff2.columns=[series,'pv1_math_mean', 'count','pv1_reading_mean',
                      'pv1_science_mean']
        
        fig = go.Figure(data=[
        go.Bar(name='China', x=dff[series], y=dff['count'], text=dff['pv1_math_mean']
               ),
        go.Bar(name='Others', x=dff2[series], y=dff2['count'], text=dff2['pv1_math_mean']
              )
        ])
        # Change the bar mode
        fig.update_layout(barmode='group',title_text= series+" against scores")
        fig.show()
        
    

    In [84]:
    for series in main_df.columns.tolist()[18:]:
        visualize(china_df, mid_score_countries, series)
    

    Talk about some of the relationships you observed in this part of the investigation. How did the feature(s) of interest vary with other features in the dataset?

    Interstingly most of the features have same behavior and patterns between chinese students and the other 3 countries, whether it be the possession of books or ICT devices, or even subjective norms of the students, nothing stands out for the chinese students answer for these features.

    Of the features you investigated, were there any unusual distributions?

    The Features that don't follow the same pattern as other countries, are the subjective norms regarding parents relationship with math, as it seems Chinese parents compared to parents from the oher countries are less likely to put more emphasize for learning math, or it could be that non chinese parents put social pressure on their children to learn math, that's why these student perform lower than Chinese students.

    Did you perform any operations on the data to tidy, adjust, or change the form of the data? If so, why did you do this?

    I have chosen 3 countries having same amount of students particpiating in the survey, to ward off any bias as possible.

    Once you're ready to finish your presentation, check your output by using nbconvert to export the notebook and set up a server for the slides. From the terminal or command line, use the following expression:

    jupyter nbconvert communicate_data_project_Part 2.ipynb --to slides --post serve --template output_toggle

    This should open a tab in your web browser where you can scroll through your presentation. Sub-slides can be accessed by pressing 'down' when viewing its parent slide. Make sure you remove all of the quote-formatted guide notes like this one before you finish your presentation

    I haven't removed this note, because it didn't work with me, I have tried to trouble shoot it but still not working, there is another alternative called RISE I have tried but it didn't give the same interactivity(hiding and unhiding the subplot slides) as desired

    References

    csv encoding: https://stackoverflow.com/questions/18171739/unicodedecodeerror-when-reading-csv-file-in-pandas-with-python

    reading large csv files : https://stackoverflow.com/questions/17444679/reading-a-huge-csv-file

    choosing non-adjacent columns : https://stackoverflow.com/questions/53052914/selecting-non-adjacent-columns-by-column-number->pandas plt legend location : https://stackoverflow.com/questions/4700614/how-to-put-the-legend-out-of-the-plot#:~:text=To%20place%20the%20legend%20outside,left%20corner%20of%20the%20legend.&text=However%2C%20a%20more%20versatile%20approach,placed%2C%20using%20the%20bbox_to_anchor%20argument.

    unstacking pandas data frame: https://cmdlinetips.com/2020/05/fun-with-pandas-groupby-aggregate-multi-index-and-unstack/#:~:text=Pandas%20unstack%20function%20to%20get%20data%20in%20wide%20form&text=When%20you%20groupby%20multiple%20variables,rows%20in%20the%20wide%20form.&text=If%20we%20want%20wide%20form,name%20to%20unstack()%20function. melting a df : https://stackoverflow.com/questions/44941082/plot-multiple-columns-of-pandas-dataframe-using-seaborn
    ordering top 10 in a plot : https://stackoverflow.com/questions/32891211/limit-the-number-of-groups-shown-in-seaborn-countplot
    using a variable in query function : https://stackoverflow.com/questions/57297077/use-variable-in-pandas-query
    correcting the country code : https://stackoverflow.com/questions/53923433/how-to-get-country-name-from-country-abbreviation-in-python-with-mix-of-alpha-2

    changing pandas value based on condition: https://www.kite.com/python/answers/how-to-change-values-in-a-pandas-dataframe-column-based-on-a-condition-in-python#:~:text=loc%20to%20change%20values%20in,for%20which%20condition%20is%20True%20.

    https://stackoverflow.com/questions/51416909/how-to-increase-the-size-of-jupyter-notebook-plot/51417061

    Plotly Bar chart : https://plotly.com/python/bar-charts/